CA2265875C - Location of a mobile station - Google Patents

Location of a mobile station Download PDF

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CA2265875C
CA2265875C CA002265875A CA2265875A CA2265875C CA 2265875 C CA2265875 C CA 2265875C CA 002265875 A CA002265875 A CA 002265875A CA 2265875 A CA2265875 A CA 2265875A CA 2265875 C CA2265875 C CA 2265875C
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location
mobile station
instance
stations
estimate
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CA2265875A1 (en
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Dennis Jay Dupray
Charles L. Karr
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02Services making use of location information
    • H04W4/025Services making use of location information using location based information parameters
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S1/00Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith
    • G01S1/02Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves
    • G01S1/022Means for monitoring or calibrating
    • G01S1/026Means for monitoring or calibrating of associated receivers
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S1/00Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith
    • G01S1/02Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves
    • G01S1/022Means for monitoring or calibrating
    • G01S1/028Simulation means, e.g. of beacon signals therefor
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009Transmission of position information to remote stations
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009Transmission of position information to remote stations
    • G01S5/0045Transmission from base station to mobile station
    • G01S5/0054Transmission from base station to mobile station of actual mobile position, i.e. position calculation on base station
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205Details
    • G01S5/021Calibration, monitoring or correction
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205Details
    • G01S5/0244Accuracy or reliability of position solution or of measurements contributing thereto
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257Hybrid positioning
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W64/00Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S2205/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S2205/001Transmission of position information to remote stations
    • G01S2205/006Transmission of position information to remote stations for emergency situations
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S2205/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S2205/001Transmission of position information to remote stations
    • G01S2205/008Transmission of position information to remote stations using a mobile telephone network
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009Transmission of position information to remote stations
    • G01S5/0018Transmission from mobile station to base station
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/06Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements

Abstract

A location system is disclosed for commercial wireless telecommunication infrastructures. The system is an end-to-end solution having one or more location centers for outputting requested locations of commercially available handsets or mobile stations (MS) based on, e.g., CDMA, AMPS, NAMPS or TDMA communication standards, for processing both local MS location requests and more global MS
location requests via, e.g., Internet communication between a distributed network of location centers. The system uses a plurality of MS
locating technologies including those based on: (I) two-way TOA and TDOA; (2) pattern recognition; (3) distributed antenna provisioning;
and (4) supplemental information from various types of very low cost non-infrastructure base stations for communicating via a typical commercial wireless base station infrastructure or a public telephone switch network.

Description

20 25 30 CA 02265875 1999-03-09 W0 98,1030-, PCT/US97l15892 LOCATION OF A MOBILE STATION FIELD OF THE INVENTION The present invention is directed generally to a system and method for locating people or objects, and in particular, to a system and method for locating a wireless mobile station using a plurality of simultaneously activated mobile station location estimators. BACKGROUND OF THE |NllENTl0N Introduction Wireless communications systems are becoming increasingly important worldwide. Wireless cellular telecommunications systems are rapidly replacing conventional wire-based telecommunications systems in many applications. Cellular radio telephone networks (“CllT"), and specialized mobile radio and mobile data radio networks are examples. The general principles of wireless cellular telephony have been described variously, for example in U. S. Patent S.29S,lB0 to Vendetti, et al. which is incorporated herein by reference. There is great interest in using existing infrastructures for wireless communication systems for locating people and/or objects in a cost effective manner. Such a capability would be invaluable in a variety of situations, especially in emergency or crime situations. Due to the substantial benefits of such a location system, several attempts have been made to design and implement such a system. Systems have been proposed that rely upon signal strength and trilateralization techniques to permit location include those disclosed in U.S. Patents 4.8l8,998 and 4,908,629 to Apsell et al. (“the Apsell patents") and 4,89l,650 to Shefler (“the Shefler patent"). However, these systems have drawbacks that include high expense in that special purpose electronics are required. Furthermore. the systems are generally only effective in line-of-sight conditions, such as rural settings. Radio wave surface reflections, relractions and ground clutter cause significant distortion. in determining the location of a signal source in most geographical areas that are more than sparsely populated. Moreover, these drawbacks are particularly exacerbated in dense urban canyon (city) areas. where errors and/or conflicts in location measurements can result in substantial inaccuracies. Another example of a location system using time of arrival and triangulation for location are satellite-based systems. such as the military and commercial versions of the Global Positioning Satellite system (“GPS"). GPS can provide accurate position determination (i.e., about l00 meters error for the commercial version of GPS) from a time-based signal received simultaneously from at least three satellites. A ground-based GPS receiver at or near the object to be located determines the difference between the time at which each satellite transmits a time signal and the time at which the signal is received and. based on the time differentials. determines the object's location. However, the GPS is impractical in many applications. The signal power levels from the satellites 10 20 25 30 CA 02265875 1999-03-09 WO 93/10307 PCT/US97/15892 are low and the GPS receiver requires a clear. line-of-sight path to at least three satellites above a horizon of about 60 degrees for effective operation. Accordingly, inclement weather conditions. such as clouds, terrain features. such as hills and trees, and buildings restrict the ability of the GPS receiver to determine its position. furthermore, the initial GPS signal detection process for a GPS receiver is relatively long (i.e., several minutes) for determining the receiver's position. Such delays are unacceptable in many applications such as, for example, emergency response and vehicle tracking. Differential GPS, or DGPS systems offer correction schemes to account for time synchronization drift. Such correction schemes include the transmission of correction signals over a two-way radio link or broadcast via FM radio station subcarriers. These systems have been found to be awkward and have met with limited success. Additionally. GPS-based location systems have been attempted in which the received GPS signals are transmitted to a central data center for performing location calculations. Such systems have also met with limited success. In brief. each of the various GPS embodiments have the same fundamental problems of limited reception of the satellite signals and added expense and complexity of the electronics required for an inexpensive location mobile station or handset for detecting and receiving the GPS signals from the satellites. Radio Propagation Background The behavior of a mobile radio signal in the general environment is unique and complicated. Efforts to perform correlations between radio signals and distance between a base station and a mobile station are similarly complex. Repeated attempts to solve this problem in the past have been met with only marginal success. factors include terrain undulations, fixed and variable clutter, atmospheric co nditions, internal radio characteristics of cellular and PCS systems. such as frequencies, antenna configurations, modulation schemes. diversity methods, and the physical geometries of direct. refracted and reflected waves between the base stations and the mobile. Noise, such as man-made externally sources (e.g., auto ignitions) and radio system co~channel and adjacent channel interference also affect radio reception and related performance measurements, such as the analog carrier-to- interference ratio (C/l), or digital energy-per-bit/Noise density ratio (Em) and are particular to various points in time and space domains. llf Propagation in free Space Before discussing real world correlations between signals and distance, it is useful to review the theoretical premise. that of radio energy path loss across a pure isotropic vacuum propagation channel, and its dependencies within and among various communications channel types. fig. l illustrates a definition of channel types arising in communications: Over the last forty years various mathematical expressions have been developed to assist the radio mobile cell designer in establishing the proper balance between base station capital investment and the quality of the radio link. typically using radio energy field- strength, usually measured in microvolts/meter, or decibels. first consider Hata‘s single ray model. A simplified radio channel can be described as: 20 25 CA 02265875 1999-03-09 W0 98/ 10307 G-,=l.P+l+L,+l.,,,+L,,-G,+G, where G», = system gain in decibels LP: lree space path loss in dB. F = fade margin in dB, L, = transmission line loss from coaxials used to connect radio to antenna. in dB, PCT lUS97l 15892 (Equation l) l,,,= miscellaneous losses such as minor antenna misalignment, coaxial corrosion, increase in the receiver noise figure due to aging, in dB. L,, = branching loss due to filter and circulator used to combine or split transmitter and receiver signals in a single antenna G,= gain of transmitting antenna G,= gain of receiving antenna free space path loss' L, as discussed in Mobile Communications Design fundamentals. William L Y. Lee. 2nd, Ed across the propagation channel is a function of distance d. lrequency l (lorl values < l Gllz, such as the 890-950 mHz cellular band): L 1 OF P. : (47mfc)2 "he" Par = received power in lree space Pt = transmitting power c = speed of light, The dillerence between two received signal powers in lree space, AP = (10) = (20) 1og(%](dB) (equation 2) (equation 3) 20 25 30 CA 02265875 1999-03-09 WO 93110307 PCT/US97/ 15892 indicates that the Tree propagation path loss is 20 dB per decade. frequencies between l Gllz and 2GHt experience increased values in the exponent. ranging from 2 to 4, or 20 to 40 dB/decade. which would be predicted for the new PCS l.8 - l.9 Gllz band. This suggests that the free propagation path loss is 20 dB per decade. However, frequencies between I GHt and 2 Gllz experience increased values in the exponent. ranging lrom2 to 4, or 10 to 40 dB/decade, which would be predicted for the new PCS L8 - L9 GHz band. One consequence from a location perspective is that the ellective range of values for higher exponents is an increased at higher frequencies, thus providing improved granularity of ranging correlation. Environmental Clutter and RF Propagation Ellects Actual data collected in real-world environments uncovered huge variations with respect to the Tree space path loss equation. giving rise to the creation of many empirical formulas for radio signal coverage prediction. Clutter, either fixed or stationary in geometric relation to the propagation oi the radio signals, causes a shadow ellect of blocking that perturbs the free space loss eiiect. Perhaps the best known model set that characterizes the average path loss is Hata's, "Empirical Formula for Propagation Loss in Land Mobile Radio", M. llata, IEEE Transactions VT-29. pp. 3|7-325, August I980, three pathloss models. based on 0kumura's measurements in and around Tokyo, “field Strength and its Variability in VHF and UHF land Mobile Service", Y. Okumura, et al, lieview of the Electrical Communications laboratory, Vol I6, pp 825-873, Sept. - Oct. I968. The typical urban llata model for LP was defined as L‘, = l.,,,: L,“ = 69.55 + 26.] 6 log(_f) ~ 13.82 log(h,_-is ) * a(hMS ) + ((44.9 — 6.55 log(H,,S ) log(d)[dB]) (Equation 4) "he" Lllu = path loss. Hata urban “BS = base station antenna height hMS= mobile station antenna height d = distance BS-MS in km 3("MS) is a correction lactor for small and medium sized cities. found to be: 25 30 CA 02265875 1999-03-09 WO 98/10307 PCT/U597/15892 1log(f —— 0.7)hMS - l.56log(f — 0.8) = cr(hM3) (Equation 5) For large cities the correction factor was found to be: a (hm) = 3.2 [log 1 1.75hMS] 2 — 4.97 (Equation 6) assuming f is equal to or greater than 400 mllz. The typical suburban model correction was found to be: f 2 L,,_"__ = L”, — 2[Iog(§-8-) ]— 5.4[dB] (Equation 7) The typical rural model modified the urban formula differently. as seen below: LEW, = LB“ - 4.78 (log :32 + 18.331og r— 40.94 [dB] (Equation 3) 20 Although the llata model was found to be useful for generaliled llf wave prediction in frequencies under I GHz in certain suburban and rural settings, as either the frequency and/or clutter increased, predictability decreased. In current practice. however, field technicians often have to make a guess for dense urban an suburban areas (applying whatever model seems best), then installing a base stations and begin taking manual measurements. Coverage problems can take up to a year to resolve. Relating Received Signal Strength to Location Having previously established a relationship between it and P,,, reference equation 2 above: cl represents the distance between the mobile station (MS) and the base station (BS); Pa, represents the received power in free space) for a given set of unchanging environmental conditions. it may be possible to dynamically measure P,,, and then determine d. in l99|, US. Patent 5.05S,8Sl to Shefler taught that if three or more relationships have been established in a triangular space of three or more base stations (BSs) with a location database constructed having data related to possible mobile station (MS) locations, then arculation calculations may be performed, which use three distinct Po, measurements to determine an X,Y, two 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT /US97I 15892 dimensional location. which can then be projected onto an area map. The triangulation calculation is based on the fact that the approximate distance of the mobile station (MS) from any base station (BS) cell can be calculated based on the received signal strength. Shefler acknowledges that terrain variations affect accuracy, although as noted above. Sheffer's disclosure does not account for a sufficient number of variables, such as fixed and variable location shadow fading, which are typical in dense urban areas with moving traffic. Most field research before about I988 has focused on characterizing (with the objective of ill coverage prediction) the ill propagation channel (i.e., electromagnetic radio waves) using a single-ray model, although standard fit errors in regressions proved dismal (e.g., 40-80 dB). Later, multi-ray models were proposed, and much later, certain behaviors were studied with radio and digital channels. in |98l,Vogler proposed that radio waves at higher frequencies could be modeled using optics principles. in I988 Walfisch and Bertoni applied optical methods to develop a two-ray model, which when compared to certain highly specific. controlled field data, provided extremely good regression lit standard errors of within L2 dB. in the Bertoni two ray model it was assumed that most cities would consist of a core of high-rise buildings surrounded by a much larger area having buildings of uniform height spread over regions comprising many square blocks, with street grids organizing buildings into rows that are nearly parallel. llays penetrating buildings then emanating outside a building were neglected. fig. 2 provides a basis for the variables. After a lengthy analysis it was concluded that path loss was a function of three factors: (I) the path loss between antennas in free space; (2) the reduction of rooftop wave fields due to settling; and (3) the effect of diffraction of the rooftop fields down to ground level. The last two factors were summarily termed lex_ given by: 2 L“ = 57.1+ A + log(f) + R — ((18 log(H))~ 18 logil — (Equation9) The influence of building geometry is contained in A: — 0g 2 - 0g 0:, tan .. 1 — MS — 51 (9)2 91 d+20lo{ [°(l H )]"l (Equation I0) However, a substantial difficulty with the two-ray model in practice is that it requires a substantial amount ol data regarding building dimensions, geometries, street widths, antenna gain characteristics for every possible ray path. etc. Additionally, it requires an inordinate amount of computational resources and such a model is not easily updated or maintained. Unfortunately, in practice clutter geometries and building heights are random. Moreover, data of sufficient detail has been extremely difficult to acquire, and regression standard lit errors are poor; i.e., in the general case. these errors were found to be 40- 60 dB. fhus the two-ray model approach, although sometimes providing an improvement over single ray techniques, still did not predict Rf signal characteristics in the general case to level of accuracy desired (< l0dB). 10 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/15892 Work by Greenstein has since developed from the perspective of measurement-based regression models, as opposed to the previous approach of predicting-first, then performing measurement comparisons. Apparemly yielding to the fact that low-power, low antenna (e.g., l2-25 feet above ground) height PCS microcell coverage was insufficient in urban buildings, Greenstein, et al. authored “Performance Evaluations for Urban Line-of-sight llicrocells Using a Mufti-ray Propagation Model", in IEEE Globecom Proceedings, l2/9|. This paper proposed the idea of fonnulating regressions based on field measurements using small PCS microcells in a lineal microcell geometry (i.e., geometries in which there is always a line—of-sight (LOS) path between a subscriber's mobile and its current microsite). Additionally. Greenstein studied the communication channels variable Bit-Error-llate (BER) in a spatial domain. which was a departure from previous research that limited field measurements to the ill propagation channel signal strength alone. However, Greenstein based his finding on two suspicious assumptions: l) he assumed that distance correlation estimates were identical for uplink and downlink transmission paths; and 2) modulation techniques would be transparent in terms of improved distance correlation conclusions. Although some data held very correlations, other data and environments produced poor results. Accordingly, his results appear unreliable for use in general location context. in l993 Greenstein, et al, authored "A Measurement-Based Model for Predicting Coverage Areas of Urban llicrocells”, in the IEEE journal On Selected Areas in Communications, Vol. II, No. 7, 9/93. Greenstein reported a generic measurement-based model of RF attenuation in terms of constant-value contours surrounding a given low-power. low antenna microcell environment in a dense. rectilinear neighborhood, such as New York City. However, these contours were for the cellular frequency band. in this case, [OS and non-LOS clutter were considered for a given microcell site. A result of this analysis was that llf propagation losses (or attenuations). when cell antenna heights were relatively low, provided attenuation contours resembling a spline plane curve depicted as an asteroid, aligned with major street grid patterns. Further. Greenstein found that convex diamond-shaped llf propagation loss contours were a common occurrence in field measurements in a rectilinear urban area. The special plane curve asteroid is represented by the formula xm + ym = rm. However, these results alone have not been sufficiently robust and general to accurately locate an MS, due to the variable nature of urban clutter spatial arrangements.. At Telesis Technology in l994 Howard Xia, et al, authored “llicrocellular Propagation Characteristics for Personal Communications in Urban and Suburban Environments", in IEEE Transactions of Vehicular Technology, Vol. 43, No. 3, 8/94, which perfonned measurements specifically in the PCS L8 to l.9 Gllz frequency band. Xia found corresponding but more variable outcome results in San Francisco, Oakland (urban) and the Sunset and Mission Districts (suburban). Summary of factors Affecting llf Propagation The physical radio propagation channel perturhs signal strength. frequency (causing rate changes, phase delay, signal to noise ratios (e.g, C/I for the analog case, or Em, , RF energy per hit. over average noise density ratio for the digital case) and Doppler-shift. Signal strength is usually characterized by: -free Space Path Loss (Lg) 15 20 CA 02265875 1999-03-09 W0 98/ 10307 PCTfUS97/ 15892 - Slow fading loss or margin (|.,,,,,,) - fast fading loss or margin (lm) Loss due to slow fading includes shadowing due to clutter blockage (sometimes included in lp). fast fading is composed of multipath reflections which cause: I) delay spread; 2) random phase shift or Rayleigh fading; and 3) random frequency modulation due to different Doppler shifts on different paths. Summing the path loss and the two fading margin loss components from the above yields a total path loss of: Lem: = Ly + Lslov + I-fut Referring to fig. 3, the figure illustrates key components of a typical cellular and PCS power budget design process. The cell designer increases the transmitted power P" by the shadow fading margin l,.,,, which is usually chosen to be within the I-2 percentile of the slow fading probability density function (PDT) to minimite the probability of unsatisfactorily low received power level P,“ at the receiver. The Pu level must have enough signal to noise energy level (e.g., I0 dB) to overcome the receiver's internal noise level (e.g., -ll8dBm in the case of cellular 0.9 GHz). for a minimum voice quality standard. Thus in the example PM must never be below -I08 dllm. in order to maintain the quality standard. Additionally the short term last signal fading due to multipath propagation is taken into account by deploying fast fading margin L,,,,, which is typically also chosen to be a few percentiles of the fast fading distribution. The l to 2 percentiles compliment other network blockage guidelines. for example the cell base station traffic loading capacity and network transport facilities are usually designed for a l-2 percentile blockage factor as well. However. in the worsuase scenario both fading margins are simultaneously exceeded, thus causing a fading margin overload. ln lloy . Steele's, text. Mobile lladio Communications, IEEE Press, l992, estimates for a GSM system operating in the L8 Gllz band with a transmitter antenna height of 6.4m and an MS receiver antenna height of 2m, and assumptions regarding total path loss. transmitter power would be calculated as follows: 20 CA 02265875 1999-03-09 WO 98110307 PCT/US97/15892 Table l: GSM Power Budget Example Parameter dBm value Will require I-slow '4 Lfast 7 llpath ll0 Min. RX pwr required -l04 TXpwr = 27 dBm Steele's sample size in a specific urban London area of 00,000 LOS measurements and data reduction found a slow fading variance of c = 7dB assuming lognormal slow fading PDF and allowing for a l.4% slow fading margin overload, thus slow = 20 : The fast fading margin was determined to be: Lfust = 7dB In contrast, Xia's measurements in urban and suburban California at La Gllz uncovered flat-land shadow fades on the order of 25-30 dB when the mobile station (MS) receiver was traveling from [OS to non-LOS geometries. In hilly terrain fades of +5 to -50 dB were experienced. Thus it is evident that attempts to correlate signal strength with MS ranging distance suggest that error ranges could not be expected to improve below I4 dB, with a high side of 25 to 50 dB. Based on 20 to 40 dB per decade, Corresponding error ranges for the distance variable would then be on the order of 900 feet to several thousand feet, depending upon the particular environmental topology and the transmitter and receiver geometries. IO 15 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/I 5392 SUMMARY OF THE INVENTION OBJECTS OF THE INVENTION It is an objective of the present invention to provide a system and method for to wireless telecommunication systems for accurately locating people and/or objects in a cost effective manner. Additionally, it is an objective of the present invention to provide such location capabilities using the measurements from wireless signals communicated between mobile stations and a network of base stations, wherein the same communication standard or protocol is utililed for location as is used by the network of base stations for providing wireless communications with mobile stations for other purposes such as voice communication and/or visual communication (such as text paging, graphical or video communications). Related objectives for the present invention include providing a system and method that: (l.l) can be readily incorporated into existing commercial wireless telephony systems with few, if any, modifications of a typical telephony wireless infrastructure; (I.2) can use the native electronics ol typical commercially available telephony wireless mobile stations (e.g., handsets) as location devices; (I3) can be used for effectively locating people and/or objects wherein there are few (if any) line-of-sight wireless receivers for receiving location signals from a mobile station (herein also denoted HS); (L4) can be used not only for decreasing location determining difficulties due to multipath phenomena but in fact uses such multipath for providing more accurate location estimates; (l.5) can be used for integrating a wide variety of location techniques in a straight-forward manner, and (L6) can substantially automatically adapt and/or (re)train and/or (re)calibrate itself according to changes in the environment and/or terrain of a geographical area where the present invention is utilized. Yet another objective is to provide a low cost location system and method. adaptable to wireless telephony systems, for using simultaneously a plurality of location techniques for synergistically increasing MS location accuracy and consistency. In particular, at least some of the following MS location techniques can be utilized by various embodiments of the present invention: (2.l) timeol-arrival wireless signal processing techniques; (2.2) time-dillerence-of-arrival wireless signal processing techniques; (23) adaptive wireless signal processing techniques having, for example, learning capabilities and including, for instance, artificial neural net and genetic algorithm processing; (2.4) signal processing techniques for matching MS location signals with wireless signal characteristics of known areas; (2.5) conflict resolution techniques for resolving conflicts in hypotheses for MS location estimates; (2.6) enhancement of MS location estimates through the use of both heuristics and historical data associating MS wireless signal characteristics with known locations and/or environmental conditions. 10 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/15892 Yet another objective is to provide location estimates in terms of time vectors, which can be used to establish motion, speed, and an extrapolated next location in cases where the MS signal subsequently becomes unavailable. DEFINITIONS The following definitions are provided for convenience. In general, the definitions here are also defined elsewhere in this document as well. (3.l) The term “wireless” herein is, in general, an abbreviation for “digital wireless". and in particular, “wireless” refers to digital radio signaling using one of standard digital protocols such as CDMA, NAMPS. AMPS. TDMA and GSM. as one skilled in the art will understand. (3.2) As used herein, the term “mobile station” (equivalently. MS) refers to a wireless device that is at least a transmitting device, and in most cases is also a wireless receiving device, such as a portable radio telephony handset. Note that in some contexts herein instead or in addition to MS, the following terms are also used: “personal station" (PS), and “location unit” (LU). In general, these terms may be considered synonymous. However, the later two terms may be used when referring to reduced functionality communication devices in comparison to a typical digital wireless mobile telephone. (33) The term. "infrastructure", denotes the network of telephony communication services, and more particularly, that portion of such a network that receives and processes wireless communications with wireless mobile stations. In panicular, this infrastmcture includes telephony wireless base stations (BS) such as those for radio mobile communication systems based on CDMA. AMPS, NAMPS, TDMA. and GSM wherein the base stations provide a network of cooperative communication channels with an air interface with the MS, and a conventional telecommunications interface with a Mobile Switch Center (MSC). Thus, an MS user within an area serviced by the base stations may be provided with wireless communication throughout the area by user transparent communication transfers (i.e., “handofls") between the user's MS and these base stations in order to maintain effective telephony service. The mobile switch center (MSC) provides communications and control connectivity among base stations and the public telephone network. (3.4) The phrase, “composite wireless signal characteristic values" denotes the result of aggregating and filtering a collection of measurements of wireless signal samples. wherein these samples are obtained from the wireless communication between an MS to be located and the base station infrastructure (e.g., a plurality of networked base stations). However, other phrases are also used herein to denote this collection of derived characteristic values depending on the context and the likely orientation of the reader. for example, when viewing these values from a wireless signal processing perspective of radio engineering, as in the descriptions of the subsequent Detailed Description sections concerned with the aspects of the present invention for receiving MS signal measurements from the base station infrastructure, the phrase typically used is: “RF signal measurements". Alternatively, l mm a data processing perspective, the phrases: “location signature cluster" and “location signal data" are used to describe signal characteristic values between the MS and the plurality of infrastructure base stations substantially simultaneously detecting MS transmissions. Moreover, since the location communications between an MS and the base station infrastructure typically include simultaneous communications with more than one base station, a related useful notion is that of a “location signature" which is the composite wireless signal I0 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/ 15892 characteristic values for signal samples between an MS to be located and a single base station. Also, in some contexts, the phrases: "signal characteristic values” or “signal characteristic data" are used when either or both a location signature(s) and/or a location signature cluster(s) are intended. SUMMARY DISCUSSION The present invention relates to a wireless mobile station location system. In particular. such a wireless mobile station location system may be decomposed into: (i) a first low level wireless signal processing subsystem for receiving, organizing and conditioning low level wireless signal measurements from a network of base stations cooperatively linked for providing wireless communications with mobile stations (MSs); and (ii) a second high level signal processing subsystem for performing high level data processing for providing most likelihood location estimates for mobile stations. More precisely. the present invention is a novel signal processor that includes at least the functionality for the high signal processing subsystem mentioned hereinabove. Accordingly, assuming an appropriate ensemble of wireless signal measurements characterizing the wireless signal communications between a particular MS and a networked wireless base station infrastructure have been received and appropriately filtered of noise and transitory values (such as by an embodiment of the low level signal processing subsystem disclosed in a cope nding PCT patent application titled, “Vfireless Location Using A Plurality of Commercial Network Infrastructures," by F. W. LeBlanc, and the present applicant(s); this copending patent application being herein incorporated by reference), the present invention uses the output from such a low level signal processing system for determining a most likely location estimate of an MS. That is, once the following steps are appropriately performed (e.g.. by the lelllanc copending application): (4.l) receiving signal data measurements corresponding to wireless communications between an MS to be located (herein also denoted the “target MS”) and a wireless telephony infrastructure; (4.2) organizing and processing the signal data measurements received from a given target MS and surrounding BS5 so that composite wireless signal clraracteristic values may be obtained from which target MS location estimates may be subsequently derived. In particular, the signal data measurements are ensembles of samples from the wireless signals received from the target MS by the base station infrastructure, wherein these samples are subsequently filtered using analog and digital spectral filtering. the present invention accomplishes the objectives mentioned above by the following steps: (4.3) providing the composite signal characteristic values to one or more MS location hypothesizing computational models (also denoted herein as “first order models” and also “location estimating models"), wherein each such model subsequently determines one or more initial estimates of the location of the target MS based on, for example, the signal processing techniques 2.l through 2.3 above. Moreover. each of the models output MS location estimates having substantially identical data structures (each such data structure denoted a "location hypothesis”). Additionally. each location hypothesis may also includes a confidence value indicating the likelihood I2 10 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCTIU S97! 15892 or probability that the target MS whose location is desired resides in a corresponding location estimate for the target M5; (4.4) adjusting or modifying location hypotheses output by the models according to. for example, 2.4 through 2.6 above so that the adjusted location hypotheses provide better target MS location estimates. In particular, such adjustments are performed on both the target MS location estimates of the location hypotheses as well as their corresponding conlidences; and (4.4) subsequently computing a “most likely" target MS location estimate for outputting to a location requesting application such as 9l| emergency, the lire or police departments, taxi services, etc. Note that in computing the most likely target l‘lS location estimate a plurality of location hypotheses may be taken into account. In fact. it is an important aspect of the present invention that the most likely MS location estimate is determined by computationally forming a composite MS location estimate utilizing such a plurality of location hypotheses so that. for example, location estimate similarities between location hypotheses can be effectively utililed. Referring now to (43) above. the filtered and aggregated wireless signal characteristic values are provided to a number of location hypothesizing models (denoted first Order Models, or folls), each of which yields a location estimate or location hypothesis related to the location of the target MS. in particular, there are location hypotheses for both providing estimates of where the target MS likely to be and where the target MS is not likely to be. Moreover. it is an aspect of the present invention that confidence values of the location hypotheses are provided as a continuous range of real numbers from, e.g., -l to l.wherein the most unlikely areas for locating the target MS are given a confidence value of -l. and the most likely areas for locating the target MS are given a confidence value of I. That is. confidence values that are larger indicate a higher likelihood that the target MS is in the corresponding MS estimated area. wherein l indicates that the target MS is absolutely NOT in the estimated area, 0 indicates a substantially neutral or unknown likelihood of the target MS being in the corresponding estimated area, and l indicates that the target MS is absolutely within the corresponding estimated area. Referring to (4.4) above. it is an aspect of the present invention to provide location hypothesis enhancing and evaluation techniques that can adjust target MS location estimates according to historical MS location data and/or adjust the confidence values of location hypotheses according to how consistent the corresponding target MS location estimate is: (a) with historical MS signal characteristic values. (b) with various physical constraims. and (c) with various heuristics. In particular. the following capabilities are provided by the present inventioru (5.l) a capability for enhancing the accuracy of an initial location hypothesis. H. generated by a first order model. FOM", by using ll as, essentially. a query or index into an historical data base (denoted herein as the location signature data base), wherein this data base includes: (a) a plurality of previously obtained location signature clusters (i.e., composite wireless signal characteristic values) such that for each such cluster there is an associated actual or verified MS locations where an MS communicated with the base station infrastmcture for locating the MS, and (b) 10 20 25 30 CA 02265875 1999-03-09 WO 93/10307 PCT/US97/15892 previous MS location hypothesis estimates l rom FOMH derived from each of the location signature clusters stored according to (a); (52) a capability lor analyzing composite signal characteristic values of wireless communications between the target MS and the base station infrastructure. wherein such values are compared with composite signal characteristics values ol known MS locations (these latter values being archived in the location signature data base). In one instance. the composite signal characteristic values used to generate various location hypotheses lorthe target MS are compared against wireless signal data of known MS locations stored in the location signature data base lor determining the reliability of the location hypothesizing models for particular geographic areas and/or environmental conditions; (5.3) a capability for reasoning about the lilteliness of a location hypothesis wherein this reasoning capability uses heuristics and constraints based on physics and physical properties of the location geography; (5.4) an hypothesis generating capability for generating new location hypotheses from previous hypotheses. As also mentioned above in (2.3), the present invention utilizes adaptive signal processing techniques. One particularly important utilization of such techniques includes the automatic tuning of the present invention so that. e.g., such tuning can be applied to adjusting the values of location processing system parameters that allect the processing perlonned by the present invention. For example, such system parameters as those used for determining the size of a geographical area to be specilied when retrieving location signal data of known MS locations from the historical (location signature) data base can substantially allect the location processing. in particular. a system parameter specifying a minimum size for such a geographical area may, if too large, cause unnecessary inaccuracies in locating an MS. Accordingly, to accomplish a tuning of such system parameters, an adaptation engine is included in the present invention lor automatically adjusting or tuning parameters used by the present invention. ‘Note that in one embodiment. the adaptation engine is based on genetic algorithm techniques. A novel aspect of the present invention relies on the discovery that in many areas where MS location services are desired, the wireless signal measurements obtained lrom communications between the target MS and the base station infrastructure are extensive enough to provide sufficiently unique or peculiar values so that the pattern of values alone may identily the location ol the target MS. further, assuming asullicient amount of such location identilying pattern information is captured in the composite wireless signal characteristic values lor a target MS, and that there is a technique lor matching such wireless signal patterns to geographical locations, then a POM based on this technique may generate a reasonably accurate target MS location estimate. Moreover, ii the present invention (e.g., the location signature data base) has captured suflicient wireless signal data from location communications between MS: and the base station infrastructure wherein the locations of the llSs are also verified and captured. then this captured data (e.g., location signatures) can be used to train or calibrate such models to associate the location of a target MS with the distinctive signal characteristics between the target MS and one or more base stations. Accordingly, the present invention includes one or more F0l1s that may be generally denoted as classilication models wherein such FOMs are trained or calibrated to associate particular composite wireless signal characteristic values with a geographical location where a target MS could likely generate the wireless signal samples lrom which the composite wireless signal characteristic values are derived. Further. the present invention includes the capability lor training (calibrating) and retraining (recalibrating) such classification F0lls to automatically I4 15 20 25 30 CA 02265875 1999-03-09 W0 98,1030-, PCT/US97/15892 maintain the accuracy of these models even though substantial changes to the radio coverage area may occur, such as the construction of a new high rise building or seasonal variations (due to, for example, foliage variations). Note that such classilication F0l’ls that are trained or calibrated to identify target MS locations by the wireless signal patterns produced constitute a particularly novel aspect of the present invention. it is well known in the wireless telephony art that the phenomenon of signal multipath and shadow fading renders most analytical location computational techniques such as time-of- arrival (TOA) or time-difference-of-arrival (T DOA) substantially useless in urban areas and particularly in dense urban areas. However, this same multipath phenomenon also may produce substantially distinct or peculiar signal measurement patterns. wherein such a pattern coincides with a relatively small geographical area. lhus, the present invention utilizes multipath as an advantage for increasing accuracy where for previous location systems multipath has been a source of substantial inaccuracies. Moreover, it is worthwhile to note that the utilitation of classification f0l‘|s in high multipath environments is especially advantageous in that high multipath environments are typically densely populated. Thus. since such environments are also capable of yielding a greater density of MS location signal data from MS: whose actual locations can be obtained, there can be a substantial amount of training or calibration data captured by the present invention for training or calibrating such classification folls and for progressively improving the HS location accuracy of such models. Moreover. since it is also a related aspect of the present invention to include a plurality stationary. low cost, low power “location detection base stations” (LBS), each having both restricted range MS detection capabilities and a built-in l1S,a grid of such LBSs can be utilired for providing location signal data (from the built-in MS) for (re)training or (re)calibrating such classification f0Ms. In one embodiment of the present invention, one or more classilication F0l1s may each include a learning module such as an artificial neural network (ANN) for associating target MS location signal data with a target MS location estimate. Additionally, one or more classification f0Ms may be statistical prediction models based on such statistical techniques as, for example, principle decomposition, partial least squares, or other regression techniques. It is a further aspect of the present invention that the personal communication system (PCS) infrastructures currently being developed by telecommunication providers offer an appropriate localized infrastructure base upon which to build various personal location systems (PLS) employing the present invention and/or utililing the techniques disclosed herein. in particular, the present invention is especially suitable for the location of people and/or objects using code division multiple access (CDMA) wireless infrastructures, although other wireless infrastructures. such as, time division multiple access (IDMA) infrastructures and GSM are also contemplated. Note that (DNA personal communications systems are described in the Telephone lndustries Association standard lS-95, for frequencies below I Gllz, and in the Wideband Spread- Spectrum Digital Cellular System Dual-llode Mobile Station-Base Station Compatibility Standard. for frequencies in the l.8-l.9 Gllz frequency bands, both of which are incorporated herein by reference. Furthermore, CDMA general principles have also been described, for example. in U. S. Patent 5,l09,390, to Gilhausen, et al, and CDMA Network Engineering Handbook by Qualcomm, lnc.. each of which is also incorporated herein by reference. Notwithstanding the above mentioned CDMA references. a brief introduction of (DNA is given here. Briefly, (DNA is an electromagnetic signal modulation and multiple access scheme based on spread spectrum communication. Each CDMA signal corresponds to an unambiguous pseudorandom binary sequence for modulating the carrier signal throughout a predetermined I5 I0 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 spectrum of bandwidth frequencies. Transmissions of individual (DNA signals are selected by correlation processing of a pseudonoise waveform. ln particular, the CDMA signals are separately detected in a receiver by using a correlator, which accepts only signal energy from the selected binary sequence and despreads its spectrum. Thus. when a lirst CDMA signal is transmitted, the transmissions of unrelated (DNA signals correspond to pseudorandom sequences that do not match the first signal. Therefore, these other signals contribute only to the noise and represent a self-interference generated by the personal communications system. As mentioned in (I .7) and in the discussion of classification T0lls above, the present invention can substantially automatically retrain and/or recalibrate itself to compensate lor variations in wireless signal characteristics (e.g., multipath) due to environmental and/or topographic changes to a geographic area serviced by the present invention. For example, in one embodiment. the present invention optionally includes low cost. low power base stations. denoted location base stations (l.BS) above, providing, for example, CDMA pilot channels to a very limited area about each such LBS. The location base stations may provide limited voice traffic capabilities, but each is capable of gathering sulficient wireless signal characteristics from an MS within the location base station's range to facilitate locating the MS. Thus. by positioning the location base stations at known locations in a geographic region such as. for instance, on street lamp poles and road signs. additional MS location accuracy can be obtained. That is, due to the low power signal output by such location base stations. lot there to be signaling control communication (e.g., pilot signaling and other control signals) between a location base station and a target MS, the MS must be relatively near the location base station. Additionally, for each location base station not in communication with the target MS. it is likely that the MS is not near to this location base station. Thus, by utilizing information received from both location base stations in communication with the target MS and those that are not in communication with the target MS, the present invention can substantially narrow the possible geographic areas within which the target MS is likely to be. further. by providing each location base station (LBS) with a co-located stationary wireless transceiver (denoted a built-in MS above) having similar functionality to an MS, the following advantages are provided: (6.l) assuming that the co-located base station capabilities and the stationary transceiver of an LBS are such that the base station capabilities and the stationary transceiver communicate with one another, the stationary transceiver can be signaled by another cornponent(s) oi the present invention to activate or deactivate its associated base station capability, thereby conserving power for the LBS that operate on a restricted power such as solar electrical power, (6.2) the stationary transceiver of an LBS can be used for translerring target MS location information obtained by the LBS to a conventional telephony base station: (6.3) since the location oi each LBS is known and can be used in location processing, the present invention is able to (re)train and/or (re)calibrate itself in geographical areas having such LBSs. That is, by activating each LBS stationary transceiver so that there is signal communication between the stationary transceiver and surrounding base stations within range, wireless signal characteristic values lor the location of the stationary transceiver are obtained for each such base station. Accordingly, such characteristic values can then be associated with the known location of the stationary transceiver for training and/or calibrating various of the location processing modules of the present invention such as the classification TOM: discussed above. in particular, such training and/or calibrating may include: (i) (re)training and/or (re)calibrating f0l1s; I6 10 20 25 30 CA 02265875 1999-03-09 W0 98,193.” PCT/US97/15892 (ii) adjusting the confidence value initially assigned to a location hypothesis according to how accurate the generating F0l’l is in estimating the location of the stationary transceiver using data obtained from wireless signal characteristics of signals between the stationary transceiver and base stations with which the stationary transceiver is capable of communicating; (iii) automatically updating the previously mentioned historical data base (i.e., the location signature data base), wherein the stored signal characteristic data for each stationary transceiver can be used for detecting environmental and/or topographical changes (e.g, a newly built high rise or other structures capable of altering the multipath characteristics of a given geographical area); and (iv) tuning of the location system parameters, wherein the steps of: (a) modifying various system parameters and (b) testing the perfomtance of the modified location system on verified mobile station location data (including the stationary transceiver signal characteristic data), these steps being interleaved and repeatedly perfonned for obtaining better system location accuracy within useful time constraints. It is also an aspect of the present invention to automatically (re)calibrate as in (63) above with signal characteristics from other known or verified locations. In one embodiment of the present invention, portable location verifying electronics are provided so that when such electronics are sufficiently near a located target MS, the electronics: (l) detect the proximity of the target MS; (ii) determine a highly reliable measurement of the location of the target MS; (iii) provide this measurement to other location determining components of the present invention so that the location measurement can be associated and archived with related signal characteristic data received from the target MS at the location where the location measurement is performed. Thus, the use of such portable location verifying electronics allows the present invention to capture and utilize signal characteristic data from verified, substantially random locations for location system calibration as in (6.3) above. Moreover. it is important to note that such location verifying electronics can verify locations automatically wherein it is unnecessary for manual activation of a location verifying process. One embodiment of the present invention includes the location verifying electronics as a “mobile (location) base station" (NBS) that can be, for example, incorporated into a vehicle, such as an ambulance, police car, or taxi. Such a vehicle can travel to sites having a transmitting target MS, wherein such sites may be randomly located and the signal characteristic data from the transmitting target MS at such a location can consequently be archived with a verified location measurement performed at the site by the mobile location base station. Moreover, it is important to note that such a mobile location base station as its name implies also includes base station electronics for communicating with mobile stations, though not necessarily in the manner of a conventional infrastructure base station. In particular, a mobile location base station may only monitor signal characteristics, such as MS signal strength, from a target MS without transmitting signals to the target MS. Alternatively. a mobile location base station can periodically be in bi-directional communication with a target NS for determining a signal time-of-arrival (or time-difference-of- arrival) measurement between the mobile location base station and the target MS. Additionally, each such mobile location base station includes components for estimating the location of the mobile location base station, such mobile location base station location estimates being important when the mobile location base station is used for locating a target MS via, for example, time-of-arrival or time-difference-of-arrival measurements as one skilled in the art will appreciate. In particular, a mobile location base station can include: 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCTIU S97/ 15892 (T.l) a mobile station (NS) for both communicating with othercomponents of the present invention (such as a location processing center included in the present invention); (7.2) a GPS receiver for determining a location of the mobile location base station; (7.3) a gyroscope and other dead reckoning devices; and (1.4) devices for operator manual entry of a mobile location base station location. furthermore. a mobile location base station includes modules for integrating or reconciling distinct mobile location base station location estimates that. for example, can be obtained using the components and devices of (TJ) through (7.4) above. That is, location estimates for the mobile location base station may be obtained from: GPS satellite data, mobile location base station data provided by the location processing center, dead reckoning data obtained from the mobile location base station vehicle dead reckoning devices, and location data manually input by an operator of the mobile location base station. The location estimating system ol the present invention offers many advantages over existing location systems. The system ol the present invention, for example. is readily adaptable to existing wireless communication systems and can accurately locate people and/or objects in a cost effective manner. in particular. the present invention requires few, if any, modifications to commercial wireless communication systems for implementation. Thus, existing personal communication system infrastructure base stations and other components of, lorexample, commercial EDNA infrastructures are readily adapted to the present invention. The present invention can be used to locate people and/or objects that are not in the line-of-sight of a wireless receiver or transmitter, can reduce the detrimental effects of multipath on the accuracy of the location estimate, can potentially locate people and/or objects located indoors as well as outdoors. and uses a numberol wireless stationary transceivers for location. The present invention employs a number of distinctly different location computational models for location which provides a greater degree of accuracy. robustness and versatility than is possible with existing systems. for instance, the location models provided include not only the radius- radius/TOA and TDOA techniques but also adaptive artificial nettral net techniques. Further, the present invention is able to adapt to the topography of an area in which location service is desired. The present invention is also able to adapt to environmental changes substantially as frequently as desired. Thus. the present invention is able to take into account changes in the location topography over time without extensive manual data manipulation. Moreover, the present invention can be utilized with varying amounts of signal measurement inputs. Thus, if a location estimate is desired in a very short time interval (e.g., less than approximately one to two seconds), then the present location estimating system can be used with only as much signal measurement data as is possible to acquire during an initial portion ol this time interval. Subsequently, after a greater amount of signal measurement data has been acquired, additional more accurate location estimates may be obtained. Note that this capability can be useful in the context ol 9ll emergency response in that a first quick course wireless mobile station location estimate can be used to route a 9| I call from the mobile station to a 9ll emergency response center that has responsibility lor the area containing the mobile station and the 9| l caller. Subsequently, once the 9ll call has been routed according to this first quick location estimate. by continuing to receive additional wireless signal measurements, more reliable and accurate location estimates of the mobile station can be obtained. Moreover, there are numerous additional advantages ol the system of the present invention when applied in CDMA communication systems. The location system of the present invention readily benefits from the distinct advantages of the CDMA 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/15892 spread spectrum scheme, namely, these advantages include the exploitation of radio frequency spectral efficiency and isolation by (a) monitoring voice activity, (b) management of two-way power control, (c) provisioning of advanced variable-rate modems and error correcting signal encoding. (cl) inherent resistance to fading, (e) enhanced privacy, and (f) multiple "ralce" digital data receivers and searcher receivers for correlation of signal multipaths. At a more general level, it is an aspect of the present invention to demonstrate the utilization of various novel computational paradigms such as: (8.!) providing a multiple hypothesis computational architecture (as illustrated best in Fig. 8) wherein the hypotheses are: (8.l.l) generated by modular independent hypothesizing computational models; (8.l1) the models are embedded in the computational architecture in a manner wherein the architecture allows for substantial amounts of application specific processing common or generic to a plurality of the models to be straightforwardly incorporated into the computational architecture; (8.l3) perfomrance of the models and according to application specific constraints and heuristics without requiring feedback loops for adjusting the models; (8.l.4) the computational architecture enhances the hypotheses generated by the models both according to past the models are relatively easily integrated into, modified and extracted from the computational architecture; (8.2) providing a computational paradigm for enhancing an initial estimated solution to a problem by using this initial estimated solution as, effectively, a query or index into an historical data base of previous solution estimates and corresponding actual solutions for deriving an enhanced solution estimate based on past performance of the module that generated the initial estimated solution. Note that the multiple hypothesis architecture provided herein is useful in implementing solutions in a wide range of applications. For example, the following additional applications are within the scope of the present invention: (9.l) document scanning applications for transforming physical documents in to electronic forms of the documents. Note that in many cases the scanning of certain documents (books, publications, etc.) may have a 20% character recognition error rate. Thus, the novel computation architecture of the present invention can be utilized by (l) providing a plurality of document scanning models as the first order models, (ii) building a character recognition data base for archiving a correspondence between characteristics of actual printed character variations and the intended characters (according to, for example, font types), and additionally archiving a correspondence of performance of each of the models on previously encountered actual printed character variations (note, this is analogous to the Signature Data Base of the MS location application described herein). and (iii) detennining any generic constraints and/or heuristics that are desirable to be satisfied by a plurality of the models. Accordingly, by comparing outputs from the first order document scanning models, a determination can be made as to whether further processing is desirable due to, for example, discrepancies between the output of the models. If further processing is desirable, then an embodiment of the multiple hypothesis architecture provided herein may be utilized to correct such discrepancies. Note that in comparing outputs lrom the first order document scanning models, these outputs may be compared at various granularities; e.g., character, sentence, paragraph or page; 10 I5 20 25 30 CA 02265875 1999-03-09 W0 M10307 PCT/US97ll5892 (9.2) diagnosis and monitoring applications such as medical diagnosis/monitoring, communication network diagnosis/monitoring; (9.3) robotics applications such as scene and/or object recognition; (9.4) seismic and/or geologic signal processing applications such as for locating oil and gas deposits; (9.5) Additionally, note that this architecture need not have all modules co-located. in particular, it is an additional aspect of the present invention that various modules can be remotely located from one another and communicate with one another via telecommunication transmissions such as telephony technologies and/or the Internet. Accordingly, the present invention is particularly adaptable to such distributed computing environments. For example, some number of the first order models may reside in remote locations and communicate their generated hypotheses via the lmernet. For instance, in weather prediction applications it is not uncommon for computational models to require large amounts of computational resources. Thus, such models running at various remote computational facilities can transfer weather prediction hypotheses (e.g., the likely path of a hurricane) to a site that performs hypothesis adjustments according to: (i) past performance of the each model; (ii) particular constraints and/or heuristics, and subsequently outputs a most likely estimate for a particular weather condition. In an alternative embodiment of the present invention, the processing following the generation of location hypotheses (each having an initial location estimate) by the first order models may be such that this processing can be provided on Internet user nodes and the first order models may reside at lnternet server sites. In this configuration, an lntemet user may request hypotheses from such remote first order models and perform the remaining processing at his/her node. In other embodiments of the present invention, a fast, abeit less accurate location estimate may be initially performed for very time critical location applications where approximate location information may be required. for example, less than l second response for a mobile station location embodiment of the present invention may be desired for 9ll emergency response location requests. Subsequently, once a relatively course location estimate has been provided, a more accurate most likely location estimate can be performed by repeating the location estimation processing a second time with, e.g., additional with measurements of wireless signals transmitted between a mobile station to be located and a network of base stations with which the mobile station is communicating, thus providing a second, more accurate location estimate of the mobile station. Additionally. note that it is within the scope of the present invention to provide one or more central location development sites that may be networked to, for example, geographically dispersed location centers providing location services according to the present invention, wherein the iolls may be accessed, substituted. enhanced or removed dynamically via network connections (via, e.g., the Internet) with a central location development site. Thus, a small but rapidly growing municipality in substantially flat low density area might initially be provided with access to, for example, two or three f0l‘ls for generating location hypotheses in the municipality's relatively uncluttered radio signaling environment. However. as the population density increases and the radio signaling environment becomes cluttered by, for example. thermal noise and multipath, additional or alternative FOMs may be transferred via the network to the location center for the municipality. 20 Ur CA 02265875 1999-03-09 W0 98/ 10307 PCT /U S97/15892 Note that in some embodiments of the present invention, since there a lack of sequencing between the FOMs and subsequent processing of location hypotheses, the FOMs can be incorporated into an expert system, if desired. For example, each POM may be activated from an antecedent of an expert system rule. Thus, the antecedent for such a rule can evaluate to TRUE if the POM outputs a location hypothesis. and the consequent portion of such a rule may put the output location hypothesis on a list of location hypotheses occurring in a particular time window for subsequent processing by the location center. Alternatively, activation of the F0lls may be in the consequents of such expert system rules. That is. the antecedent of such an expert system rule may determine if the conditions are appropriate for invoking the FOM(s) in the rule's consequent. Of course, other software architectures may also to used in implementing the processing of the location center without departing from scope of the present invention. In particular. object-oriented architectures are also within the scope of the present invention. for example. the F0lls may be obiect methods on an MS location estimator object, wherein the estimator object receives substantially all target MS location signal data output by the signal filtering subsystem. Alternatively, software bus architectures are contemplated by the present invention, as one skilled in the art will understand, wherein the software architecture may be modular and facilitate parallel processing. further features and advantages of the present invention are provided by the figures and detailed description accompanying this invention summary. 2| 10 I5 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCI‘ /US97/ 15892 BRIEF DESCRIPTION OF THE DRAWINGS Fig. I illustrates various perspectives of radio propagation opportunities which may be considered in addressing correlation with mobile to base station ranging. Fig. 2 shows aspects of the two-ray radio propagation model and the effects of urban clutter. Fig.3 provides a typical example of how the statistical power budget is calculated in design of a Commercial Mobile Radio Service Provider network Fig. 4 illustrates an overall view of a wireless radio location network architecture, based on MN principles. Fig. 5 is a high level block diagram of an embodiment of the present invention for locating a mobile station (MS) within a radio coverage area for the present invention. Fig. 6 is a high level block diagram of the location center I42. Fig.7 is a high level block diagram of the hypothesis evaluator for the location center. Fig. 8 is a substantially comprehensive high level block diagram illustrating data and control flows between the components of the location center. as well the functionality of the components. Fig. 9 is a high level data structure diagram describing the fields of a location hypothesis object generated by the first order models I224 of the location center. Fig. I0 is a graphical illustration of the computation performed by the most likelihood estimator l344 of the hypothesis evaluator. Fig. ll is a high level block diagram of the mobile base station (NBS). Fig. I2 is a high level state transition diagram describing computational states the Mobile Base station enters during operation. fig. l3 is a high level diagram illustrating the data structural organilation of the Mobile Base station capability for autonomously determining a most likely NBS location from a plurality of potentially conflicting MBS location estimating sources. Fig. l4 shows one method of modeling CDMA delay spread measurement ensembles and interfacing such signals to a typical artificial neural network based FOM. Fig. l5 illustrates the nature of RF “Dead lanes" . notch area, and the importance of including location data signatures from the backside of radiating elements. Figs. l6a through l6c present a table providing a brief description of the attributes of the location signature data type stored in the location signature data base I320. Figs. l7a through I7: present a high level flowchart of the steps performed by function, “UPDATE_LOC_SlG_‘D8," for updating location signatures in the location signature data base I320; note, this flowchart corresponds to the description of this function in APPENDIX C. I0 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 figs. l8a thmugh l8b present a high level flowchart of the steps performed by function, “REDIlCE_BAD_Dl3_l.OC_SlGS." for updating location signatures in the location signature data base I320; note, this flowchart corresponds to the description of this function in APPENDIX C. figs. I93 through l9b present a high level flowchart of the steps performed by function. “lNCltEASE_CONF|DENCE_0F_GO0D_DB_LOC_SlGS,” for updating location signatures in the location signature data base I320; note, this flowchart corresponds to the description of this function in APPENDIX C. figs. 20a through 2Dd present a high level flowchart of the steps performed by function, “DEIEIIMINE_l0CAflON_S|GNAfUlIE_flT__ElllI0ltS." for updating location signatures in the location signature data base I320; note, this flowchart corresponds to the description of this function in APPENDIX C. fig. 2| presents a high level flowchart of the steps performed by function, “ESTll‘lATE_LOC__SlG_fll0l1_DB,” for updating location signatures in the location signature data base I320; note. this flowchart corresponds to the description of this function in APPENDIX C. figs. 22a through 22b present a high level flowchart of the steps performed by function. “GET_AlIEA_fO_SEARCH,” for updating location signatures in the location signature data base I320; note. this flowchart corresponds to the description of this function in APPENDIX C. Figs. 23a through 23b present a high level flowchart of the steps performed by function, “GET_DlFFEllENCE_MEASUllEMENf," for updating location signatures in the location signature data base I320; note, this flowchart corresponds to the description of this function in APPENDIX C. Fig. 24 is a high level illustration of context adjuster data structures and their relationship to the radio coverage area for the present invention: figs. 252 through 25b present a high level flowchart of the steps performed by the function, "CON'fEXT_AD]USTElI," used in the context adjuster I326 for adjusting mobile station estimates provided by the first order models I224; this flowchart corresponds to the description of this function in APPENDIX D. figs. 26a through 26c present a high level flowchart of the steps performed by the function, “GEl'_ADjUSTED_l0C_llYP_LlST_fOll," used in the context adjuster I326 for adjusting mobile station estimates provided by the first order models I224; this flowchart corresponds to the description of this function in APPENDIX D. Figs. 27a through 27b present a high level flowchart of the steps performed by the function. “CONflDENCE_ADJUSTEll." used in the context adjuster I326 for adjusting mobile station estimates provided by the first order models I224; this flowchart corresponds to the description of this function in APPENDIX D. Fig. 28a and 28b presents a high level flowchart of the steps performed by the function. "GE'f_C0ffPOSlTE_PllEDlCTl0N_llAPPED_ CLUSTER__DENS!TY," used in the context adjuster I326 for adjusting mobile station estimates provided by the first order models I224; this flowchart corresponds to the description of this function in APPENDIX D. CA 02265875 1999-03-09 WO 98110307 PCT/US97/15892 Figs. 293 through 29h present a high level flowchart ol the steps performed by the lunction, “GET_Pl\EDlCi l0N_HAPPED_CLUSIEli_DEllSlTY__F0ll,” used in the context adjuster I326 for adjusting mobile station estimates provided by the first order models I224: this llowchart corresponds to the description of this function in APPENDIX D. Fig. 30 illustrates the primary components of the signal processing subsystem. Fig. 3| illustrates how automatic pmvisioning of mobile station information from multiple CMRS occurs. 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97Il 5892 DETAILED DESCRIPTION Detailed Description introduction Various digital wireless communication standards have been introduced such as Advanced Mobile Phone Service (AMPS), llarrowband Advanced Mobile Phone Service (llAMPS), code division multiple access (CDMA) and Time Division Multiple Access (T DMA) (e.g., Global Systems Mobile (GSM). These standards provide numerous enhancements for advancing the quality and communication capacity for wireless applications. Referring to CDMA, this standard is described in the felephone Industries Association standard IS- 95, for frequencies below I GHz, and in J-STD-008, the Wrdeband Spread-Spectrum Digital Cellular System Dual-Mode Mobile Station-Base station Compatibility Standard. for frequencies in the L8 - l.9 GHZ frequency bands. Additionally, CDMA general principles have been described, for example, in U.S. Patent 5,l09,390, Diversity Receiver in a CDMA Cellular Telephone System by Gilhousen. There are numerous advantages of such digital wireless technologies such as CDMA radio technology. for example. the CDMA spread spectrum scheme exploits radio frequency spectral efficiency and isolation by monitoring voice activity, managing two- way power control, provision of advanced variable-rate modems and error correcting signal design, and includes inherent resistance to fading, enhanced privacy, and provides for multiple "rake" digital data receivers and searcher receivers for correlation of multiple physical propagation paths, resembling maximum likelihood detection, as well as support for multiple base station communication with a mobile station, i.e., soft or softer hand-off capability. When coupled with a location center as described herein, substantial improvements in radio location can be achieved. For example, the CDMA spread spectrum scheme exploits radio frequency spectral efficiency and isolation by monitoring voice activity, managing two-way power control, provision of advanced variable-rate modems and errorcorrecting signal design, and includes inherent resistance to fading, enhanced privacy, and provides for multiple "rake" digital data receivers and searcher receivers for correlation of multiple physical propagation paths, resembling maximum likelihood detection, as well as support for multiple base station communication with a mobile station, i.e., soft hand-off capability. Moreover, this same advanced radio communication infrastructure can also be used for enhanced radio location. As a further example, the capabilities of lS-4l and AIN already provide a broad-granularity of wireless location, as is necessary to, for example, properly direct a terminating call to an MS. Such information, originally intended for call processing usage, can be reused in conjunction with the location center described herein to provide wireless location in the large (i.e., to determine which country. state and city a particular MS is located) and wireless location in the small (i.e., which location, plus or minus a few hundred leer within one or more base stations a given MS is located). Fig.4 is a high level diagram of a wireless digital radiolocation intelligent network architecture for the present invention. Accordingly, this figure illustrates the interconnections between the components. for example, of a typical PCS network configuration and various components that are specific to the present invention. in particular, as one skilled in the art will understand, a typical wireless (PCS) network includes: (a) a (large) plurality of conventional wireless mobile stations (MSs) I40 for at least one of voice related communication, visual (e.g., text) related communication, and according to present invention, location related communication; (b) a mobile switching center (MSC) ill; 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 (c) a plurality of wireless cell sites in a radio coverage area I20, wherein each cell site includes an infrastructure base station such as those labeled I22 (ur variations thereof such as I22A - I220). In particular, the base stations I22 denote the standard high traffic. fixed location base stations used for voice and data communication with a plurality of ms I40. and, according to the present invention, also used for communication of information related to locating such IISs I40. Additionally, note that the base stations labeled I52 are more directly related to wireless location enablement. For example. as described in greater detail hereinbelow, the base stations I52 may be low cost. low functionality transponders that are used primarily in communicating MS location related information to the location center I42 (via base stations I22 and the IISC Ill). Note that unless stated otherwise, the base stations I52 will be referred to hereinafter as “location base station(s) I52” or simply “I.BS(s) I52"); (d) a public switched telephone network (PSTII) I24 (which may include signaling system links I06 having network control components such as: a service control point (SCP) I04 , one or more signaling transfer points (STPs) No. Added to this wireless network, the present invention provides the following additional components: (I0.l) a location center I42 which is required for determining a location of a target MS I40 using signal characteristic values for this target MS; (l0.2) one or more mobile base stations I40 (IIBS) which are optional, for physically traveling toward the target MS I40 or tracking the target MS; (I03) a plurality of location base stations I52 (LBS) which are optional, distributed within the radio coverage areas I20, each LBS I52 having a relatively small MS I40 detection area I54; Since location base stations can be located on potentially each floor of a multi-story building, the wireless location technology described herein can be used to perform location in temts of height as well as by latitude and longitude. In operation, the MS I40 may utilize one of the wireless technologies, CDIIA, IDMA, AMPS. IIAMPS or GSI1 techniques for radio communication with: (a) one or more infrastructure base stations I22, (b) mobile base station(s) I48 , (c) an LBS I52. lleferring to Fig. 4 again, additional detail is provided of typical base station coverage areas. sectorization, and high level components within a radio coverage area I20, including the MSC II2. Although base stations may be placed in any configuration, a typical deployment configuration is approximately in a cellular honeycomb pattern, although many practical tradeolls exist, such as site availability, versus the requirement for maximal terrain coverage area. To illustrate, three such exemplary base stations (BSs) are l22A, I220 and l22C, each of which radiate referencing signals within their area of coverage I69 to facilitate mobile station (MS) I40 radio frequency connectivity, and various timing and synchronization functions. Note that some base stations may contain no sectors I30 (e.g. l22E), thus radiating and receiving signals in a 360 degree omnidirectional coverage area pattern, or the base station may contain "smart antennas" which have specialized coverage area patterns. However, the generally most frequent base stations I22 have three sector I30 coverage area patterns. For example, base station l22A includes sectors I30, additionally labeled a, b and c. Accordingly, each of the sectors I30 radiate and receive signals in an approximate I20 degree arc, from an overhead view. As one skilled in the art will understand. actual base station coverage areas I69 (stylistically represented by hexagons about the base 26 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT /U S97ll5892 stations I22) generally are designed to overlap to some extent, thus ensuring seamless coverage in a geographical area. Contml electronics within each base station I22 are used to communicate with a mobile stations I40. Information regarding the coverage area for each sector I30, such as its range, area, and "holes" or areas of no coverage (within the radio coverage area I20), may be known and used by the location center I42 to facilitate location determination. Further, during communication with a mobile station I40, the identification of each base station I22 communicating with the MS I40 as well, as any sector identification information, may be known and provided to the location center I42. In the case of the base station types I22, I48, and I52 communication of location infomtation, a base station or mobility controller I74 (BSC) controls, processes and provides an interface between originating and terminating telephone calls from/to mobile station (MS) I40, and the mobile switch center (IISC) Ill. The MSC I22, on-the-other-hand, performs various administration functions such as mobile station I40 registration, authentication and the relaying of various system parameters, as one skilled in the art will understand. The base stations I22 may be coupled by various transport facilities I16 such as leased lines, frame relay, T-Carrier links, optical fiber links or by microwave communication links. When a mobile station I40 (such as a CDl1ll,AIIPS,NI\l1PS mobile telephone) is powered on and in the idle state, it constantly monitors the pilot signal transmissions from each of the base stations I22 located at nearby cell sites. Since base station/sector coverage areas may often overlap, such overlapping enables mobile stations I40 to deoect, and, in the case of certain wireless technologies, communicate simultaneously along both the forward and reverse paths, with multiple base stations I22 and/or sectors I30. In Fig. 4 the constantly radiating pilot signals Irom base station sectors I30, such as sectors a, b and c of BS l22A, are detectable by mobile stations I40 within the coverage area I69 for BS l22A. That is, the mobile stations I40 scan for pilot channels, corresponding to a given base station/sector identifiers (IDs) , for determining which coverage area I69 (i.e., cell) it is contained. This is performed by comparing signals strengths of pilot signals transmitted from these particular cell-sites. The mobile station I40 then initiates a registration request with the MSC ll2, via the base station controller I74. The MSC ll2 determines whetheror not the mobile station I40 is allowed to proceed with the registration process (except in the case of a 9M call, wherein no registration process is required). At this point calls may be originated from the mobile station I40 or calls or short message service messages can be received from the network. The NSC l|2 communicates as appropriate, with a class 4/5 wireline telephony circuit switch or other central olfices, connected to the PSTN I24 network. Such central offices connect to wireline terminals, such as telephones, or any communication device compatible with the line. The PSTN I24 may also provide connections to long distance networks and other networks. The MSC ll2 may also utilize IS/4I data circuits or trunks connecting to signal transfer point lI0, which in turn connects to a service control point I04, via Signaling System #7 (SS7) signaling links (e.g., trunks) for intelligent call processing, as one skilled in the art will understand. In the case of wireless Alll services such links are used for call routing instructions of calls interacting with the MSC In or any switch capable of providing service switching point functions, and the public switched telephone network (PSTII) I24, with possible termination back to the wireless network 15 20 25 30 CA 02265875 1999-03-09 WO 98110307 PCT/US97/1 5892 Referring to Fig. 4 again, the location center (LC) I42 interfaces with the NSC ll2 either via dedicated transport facilities I78. using for example, any number of LAN/WAN technologies, such as Ethernet, last Ethernet, frame relay, virtual private networks, etc., or via the PSIII I24. The LC I42 receives autonomous (e.g., unsolicited) command/response messages regarding, lor example: (a) the state of the wireless network of each service provider, (b) NS I40 and BS I22 radio frequency (Ill) measurements, (c) any NBSS I48, (d) location applications requesting NS locations using the location center. Conversely, the LC I42 provides data and control information to each of the above components in (a) - (d). Additionally, the LC I42 may provide location information to an MS I40, via a BS I22. Moreover, in the case of the use of a mobile base station (NBS) I48, several communications paths may exist with the LC I42. The NBS I48 acts as a low cost, partially-functional, moving base station, and is, in one embodiment, situated in a vehicle where an operator may engage in NS I40 searching and tracking activities. In providing these activities using (DNA, the NBS I48 provides a forward link pilot channel fora target NS I40, and subsequently receives unique BS pilot strength measurements from the NS I40. The NBS I43 also includes a mobile station for data communication with the LC I42, via a BS I22. In particular, such data communication includes telemetering the geographic position ol the NBS I48 as well as various Ill measurements related to signals received from the target NS I40. In some embodiments, the NBS I48 may also utilize multiple-beam fixed antenna array elements and/or a moveable narrow beam antenna , such as a microwave dish I82. fhe antennas lor such embodiments may have a known orientation in order to further deduce a radio location of the target NS I40 with respect to an estimated current location of the NBS I48. As will be described in more detail herein below. the NBS I48 may further contain a global positioning system (GPS), distance sensors, dead-reckoning electronics, as well as an on-board computing system and display devices for locating both the NBS I48 of itself as well as tracking and locating the target I15 I40. The computing and display provides a means for communicating the position ol the target NS I40 on a map display to an operator of the NBS I43. Each location base station (LBS) I52 is a low cost location device. Each such LBS I52 communicates with one or more of the infrastructure base stations I22 using one or more wireless technology interface standards. In some embodiments, to provide such LBS‘: cost effectively, each LBS I52 only partially or minimally supports the air-interface standards of the one or more wireless technologies used in communicating with both the BSs I22 and the NSs I40. Each LBS I52, when put in service, is placed at a fixed location, such as at a traffic signal, lamp post, etc., and wherein the location ol the LBS may be determined as accurately as, for example, the accuracy of the locations of the infrastructure BSs I22. Assuming the wireless technology CDNA is used, each BS I22 uses a time oflset of the pilot PN sequence to identify a lorward CDNA pilot channel. In one embodiment, each LBS I52 emits a unique, time-offset pilot PN sequence channel in accordance with the (DNA standard in the BF spectrum designated lor BSs I22, such that the channel does not interfere with neighboring BSs I22 cell site channels, nor would it interfere with neighboring LBSs I52. However, as one skilled in the art will understand, time olfsets, in CDNA chip sizes. may be re-used within a PCS system. thus providing efficient use ol pilot time offset chips, thereby achieving spectrum efficiency. Each LBS I52 may also contain multiple wireless receivers in order to monitor transmissions from a target NS I40. Additionally, each LBS I52 contains mobile station I40 electronics, thereby allowing the LBS to both be controlled by the LC I42, and to transmit information to the LC I42, via at least one neighboring BS I22. 20 25 30 CA 02265875 1999-03-09 wo 9a/10307 PCT/US97/15892 As mentioned above, when the location of a particular target MS l40 is desired, the LC I42 can request location information about the target MS I40 from, for instance. one or more activated LBSs I52 in a geographical area of interest. Accordingly, whenever the target MS I40 is in such an area, or is suspected of being in the area, either upon command fmm the LC I42, or in a substantially continuous fashion. the LBS's pilot channel appears to the target MS I40 as a potential neighboring base station channel, and consequently, is placed. for example, in the CDMA neighboring set, or the CDMA remaining set, of the target MS MO (as one familiar with the CDMA standards will understand). During the normal (DNA pilot search sequence of the mobile station initialization state (in the target MS), the target MS MO will, if within range of such an activated LBS I52, detect the LBS pilot presence during the CDMA pilot channel acquisition substate. Consequently. the target MS I40 performs lll measurements on the signal from each detected LBS l$2. Similarly, an activated LBS I52 can perform llf measurements on the wireless signals from the target MS I40. Accordingly, each LBS I52 detecting the target MS I40 may subsequently telemeter back to the LC I42 measurement results related to signals from/to the target MS I40. Moreover, upon command. the target MS MO will telemeter back to the LC I42 its own measurements of the detected l.BSs I52, and consequently, this new location infomiation, in conjunction with location related information received from the BSs I22, can be used to locate the target MS I40. it should be noted that an LBS I52 will normally deny hand-off requests. since typically the LBS does not require the added complexity of handling voice or traffic bearer channels, although economics and peak traffic load conditions would dictate preference here. GPS timing infomiation. needed by any CDMA base station, is either achieved via a the inclusion of a local GPS receiver or via a telemetry process from a neighboring conventional BS l22, which contains a GPS receiver and timing information. Since energy requirements are minimal in such an LBS l52, (rechargeable) batteries or solar cells may be used to power the LBS. llo expensive terrestrial transport link is typically required since two-way communication is provided by the included MS MO (or an electronic variation thereof). fhus, LBS: lS2 may be placed in numerous locations. such as: (a) in dense urban canyon areas (e.g., where signal reception may be poor and/or very noisy); (b) in remote areas (e.g., hiking, camping and skiing areas); (c) along highways (e.g., for emergency as well as monitoring traffic flow), and their rest stations; or (d) in general, wherever more location precision is required than is obtainable using other wireless inlrastruction network C0l'|1p0flBfllS. Location Center - Network Elements API Description A location application programming interface I36 (fig. 4), or L-llP|, is required between the location center I42 (LC) and the mobile switch center (NSC) network element type, in order to send and receive various control, signals and data messages. The L- AP! should be implemented using a preferably high-capacity physical layer communications interface, such as lEEE standard 8023 (ll) basel Ethernet). although other physical layer interfaces could be used. such as fiber optic ATM, frame relay, etc. fwo fonns of AP! implementation are possible. In the first case the signals control and data messages are realized using the NSC ll2 vendor's 29 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97Il5892 native operations messages inherent in the product offering. without any special modifications. In the second case the LA?! includes a lull suite ol commands and messaging content specifically optimized for wireless location purposes. which may require some, although minor development on the pan of the NSC vendor. Signal Processor Description llelerring to Fig. 30, the signal processing subsystem receives control messages and signal measurements and transmits appmpriate control messages to the wireless network via the location applications programming interface referenced earlier. for wireless location purposes. The signal processing subsystem additionally provides various signal idintification, conditioning and pre- processing functions, including bullering, signal type classification. signal filtering. message control and routing lunctions to the location estimate modules. There can be several combinations of Delay Spread/Signal Strength sets of measurements made available to the signal processing subsystem 20. In some cases the mobile station I40 (Fig. l) may be able to detect up to three or tour Pilot Channels representing three to four Base Stations, or as lew as one Pilot Channel. depending upon the environment. Similarly, possibly more than one BS l22 can detect a mobile station I40 transmitter signal, as evidenced by the provision of cell diversity or salt hand-oll in the CDMA standards. and the fact that multiple CllllS' base station equipment commonly will overlap coverage areas. For each mobile station l40 or BS I22 transmitted signal detected by a receiver group at a station. multiple delayed signals, or “fingers” may be detected and tracked resulting lmm multipath radio propagation conditions, from a given transmitter. In typical spread spectrum diversity CD MA receiver design. the “first” finger represents the most direct, or least delayed multipath signal. Second or possibly third or lourth fingers may also be detected and tracked. assuming the mobile station contains a sufficient number ol data receivers. Although traditional TDA and TDOA methods would discard subsequent fingers related to the same transmitted finger, collection and use of these additional values can prove useful to reduce location ambiguity, and are thus collected by the Signal Processing subsystem in the location Center I42. From the mobile receiver‘: perspective, a number of combinations ol measurements could be made available to the Location Center. Due to the disperse and near-random nature of CDMA radio signals and propagation characteristics,traditional TDA/TDOA location methods have failed in the past. because the number of signals received in dillerent locations area dillerent. In a particularly small urban area, say less than 500 square feet, the number ol lll signals and there multipath components may vary by over I00 percent. Due to the large capital outlay costs associated with providing three or more overlapping base station coverage signals in every possible location, most practical digital PCS deployments result in fewer than three base station pilot channels being reportable in the majority of location areas. thus resulting in a larger, more amorphous location estimate. This consequence requires a family ol location estimate location modules, each firing whenever suitable data has been presented to a model, thus providing a location estimate to a backend subsystem which resolves ambiguities. In one embodiment of this invention using backend hypothesis resolution, by utilizing existing knowledge concerning base station coverage area boundaries (such as via the compilation a lll coverage database - either via RF coverage area simulations or 30 10 15 20 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 field tests), the location error space is decreased. Negative logic Venn diagrams can be generated which deductively rule out certain location estimate hypotheses. Although the fonvard link mobile station's received relative signal strength (RRSS35) of detected nearby base station transmitter signals can be used directly by the location estimate modules, the [DNA base station's reverse link received relative signal strength (RRSSH5) of the detected mobile station transmitter signal must be modified prior to location estimate model use, since the mobile station transmitter power level changes nearly continuously. and would thus render relative signal strength useless for location purposes. One adjustment variable and one factor value are required by the signal processing subsystem in the CDHA air interface case: I.) instantaneous relative power level in dBm (IRPL) of the mobile station transmitter. and 2.) the mobile station Power Class. By adding the lllPl. to the llllSS,,;, a synthetic relative signal strength (SRSSHS) of the mobile station I40 signal detected at the BS I22 is derived, which can be used by location estimate model analysis, as shown below: SllSS,,5 = RRSSH; + lliPl (in dBm) SRSS,,;_ a corrected indication of the effective path loss in the reverse direction (mobile station to BS), is now comparable with llllSSa; and can be used to provide a correlation with either distance or shadow fading because it now accounts for the change of the mobile station transmitter‘s power level. fhe two signals RRSSBS and SRSSH, can now be processed in a variety of ways to achieve a more robust correlation with distance or shadow fading. Although Rayleigh fading appears as a generally random noise generator, essentially destroying the correlation value of either RRSS35 or SRSSH; measurements with distance individually, several mathematical operations or signal processing functions can be performed on each measurement to derive a more robust relative signal strength value, overcoming the adverse Rayleigh fading effects. Examples include averaging, taking the strongest value and weighting the strongest value with a greater coefficient than the weaker value, then averaging the results. This signal processing technique takes advantage of the fact that although a Rayleigh fade may often exist in either the forward or reverse path, it is much less probable that a Rayleigh fade also exists in the reverse or fonvard path, respectively. A shadow fade however. similiarly affects the signal strength in both paths. At this point a CDMA radio signal direction-independent "net relative signal strength measurement" is derived which is used to establish a correlation with either distance or shadow fading, or both. Although the ambiguity of either shadow fading or distance cannot be determined. other means can be used in conjunction, such as the fingers of the CDMA delay spread measurement, and any other TOA/lD0ll calculations from other geographical points. In the case of a mobile station with a certain amount of shadow fading between its BS I22 (Fig. 2), the first finger of a CDMA delay spread signal is most likely to be a relatively shorter duration than the case where the mobile station I40 and BS I22 are separated by a greater distance, since shadow fading does not materially affect the arrival time delay of the radio signal. By performing a small modification in the control electronics of the (DNA base station and mobile station receiver circuitry, it is possible to provide the signal processing subsystem 20 (reference Fig. 30) within the Location scenter I42 (Fig. l) with 3| 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/U S97] 15892 data that exceed the one-to-one CDMA delay-spread fingers to data receiver correspondence. Such additional information, in the form of additional CDMA fingers (additional multipath) and all associated detectable pilot channels, provides new information which is used to enhance to accuracy of the Location Center's location estimate location estimate modules. This enhanced capability is provided via a control message, sent from the Location center l42 to the mobile switch center I2. and then to the base station(s) in communication with. or in close proximity with, mobile stations I40 to be located. Two types of location measurement request control messages are needed: one to instruct a target mobile station I40 (i.e., the mobile station to be located) to telemeter its BS pilot channel measurements back to the primary BS I22 and fmm there to the mobile switch center II2 and then to the location system 42. The second control message is sent from the location system 42 to the mobile switch center ll 2, then to first the primary BS, instructing the primary BS‘ searcher receiver to output (i.e., return to the initiating request message source) the detected target mobile station I40 transmitter CDMA pilot channel offset signal and their corresponding delay spread finger (peak) values and related relative signal strengths. The control messages are implemented in standard mobile station M0 and BS I22 CDMA receivers such that all data results fmm the search receiver and multiplexed results from the associated data receivers are available for transmission back to the Location Center I42. Appropriate value ranges are required regarding mobile station I40 parameters T_ADD,, T_D li0P,, and the ranges and values for the Active. Neighboring and Remaining Pilot sets registers, held within the mobile station I40 memory. Further mobile station I40 receiver details have been discussed above. In the normal case without any specific multiplexing means to provide location measurements. exactly how many CDHA pilot channels and delay spread lingers can or should be measured vary according to the number of data receivers contained in each mobile station l40. As a guide, it is preferred that whenever Rf characteristics permit, at least three pilot channels and the strongest lirst three fingers. are collected and processed. from the BS I22 perspective, it is preferred that the strongest first four CDMA delay spread fingers and the mobile station power level be collected and sent to the location system 42, for each of preferably three BSs I22 which can detect the mobile station I40. A much larger combination of measurements is potentially feasible using the extended data collection capability of the CDMA receivers. Fig. 30 illustrates the components of the Signal Processing Subsystem. The main components consist of the input queue(s) 7, signal classifier/filter 9, digital signaling processor I7, imaging filters l9, output queue(s) 2|. router/distributor 23, a signal processor database 26 and a signal processing controller I5. Input queues 7 are required in order to stage the rapid acceptance of a significant amount of Rf signal measurement data. used for either location estimate purposes or to accept autonomous location data. Each location request using fixed base stations may, in one embodiment, contain from I to I28 radio frequency measurements from the mobile station, which translates to approximately 6f .44 kilobytes of signal measurement data to be collected within l0 seconds and I28 measurements from each of possibly four base stations. or 245.76 kilobytes for all base stations, for a total of approximately 640 signal measurements from the five sources, or 307.2 kilobytes to arrive per mobile station location request in I0 seconds. An input queue storage space is assigned at the moment a location request begins, in order to establish a formatted data structure in persistent store. Depending upon the 10 15 20 25 30 CA 02265875 1999-03-09 W0 93/10307 PCT/US97/15892 urgency of the time required to render a location estimate, fewer or more signal measurement samples can be taken and stored in the input queue(s) T accordingly. The signal processing subsystem supports a variety of wireless network signaling measurement capabilities by detecting the capabilities of the mobile and base station through messaging structures provided bt the location application programming interface. Detection is accomplished in the signal classifier 9 (Fig. 30) by referencing a mobile station database table within the signal processor database 26. which provides, given a mobile station identification number, mobile station revision code. other mobile station charactersitics. Similiarly, a mobile switch center table 3l provides MSC characteristics and identifications to the signal classifier/filter 9. The signal classifier/filter adds additional message header information that further classifies the measurement data which allows the digital signal processor and image filter components to select the proper internal processing subcomponents to perform operations on the signal measurement data, for use by the location estimate modules. Regarding service control point messages autonomously received from the input queue 7, the signal classifier/filter9 detemines via a signal processing database 26 query that the message is to be associated with a home base station module. Thus appropriate header information is added to the message, thus enabling the message to pass through the digital signal processor I7 unaffected to the output queu 2i. and then to the router/distributor 23. The router/distributor 23 then routes the message to the HBS first order model. Those skilled in the art will understand that associating location requests from Home Base Station configurations require substantially less data: the mobile identification number and the associated wireline telephone number transmission from the home location register are on the order of less than 32 bytes. Consequentially the home base station message type could be routed without any digital signal processing. Output queue(s) Zl are required for similar reasons as input queues 7: relatively large amounts of data must be held in a specific format for further location processing by the location estimate modules. The router and distributor component 23 is responsible to directing specific signal measurement data types and structures to their appropriate modules. For example. the HBS FOM has no use for digital filtering structures. whereas the TDOA module would not be able to process an HBS response message. The contmller l5 is responsible for staging the movement of data among the signal processing subsystem 20 components input queue 7, digital signal processor l7. router/distributor 23 and the output queue 2|, and to initiate signal measurments within the wireless network. in response from an internet |68 location request message in fig. I, via the location application programming interface. In addition the controller IS receives autonomous messages from the MSC . via the location applications programming interface (Fig. l) or l-API and the input queue 7, whenever a 9-l-l wireless call is originated. The mobile switch center provides this autonomous notification to the location system as follows: By specifiying the appropriate mobile switch center operations and maintenance commands to surveil calls based on certain digits dialed such as 9-l-l. the location applications programming interface, in communications with the llSCs, receives an autonomous notification whenever a mobile station user dials 9-l —l. Specifically, a bi- directional authorized communications port is configured, usually at the operations and maintenance subsystem of the MSCs, or with their associated network element manager system(s), with a data circuit. such as a DS-l, with the location applications programming 33 10 20 25 30 CA 02265875 1999-03-09 wo 93/19307 PCT/US97/15892 interface in fig. I. Next, the “call trace" capability of the mobile switch center is activated for the respective communications port. The exact implementation of the vendor-specific man-machine or Open Systems lmerface (OSl) commands(s) and their associated data structures generally vary among MSC vendors. however the trace function is generally available in various forms, and is required in order to comply with federal Bureau of investigation authorities for wire tap purposes. After the appropriate surveillance commands are established on the NSC, such 9-l-l call notifications messages containing the mobile station identification number (Mill) and, in phase I E9-l-l implementations, a pseudo-automatic number identication (a.l<.a. plllll) which provides an association with the primary base station in which the 9-l-I caller is in communicaiton. In cases where the pANl is known from the onset, the signal processing subsystem avoids querying the MSC in question to determine the primary base station identification associated with the 9-l-I mobile station caller. After the signal processing controller I5 receives the first message type, the autonomous notification message from the mobile switch center ll2 to the location system 42, containing the mobile identification number and optionally the primary base station identification. the controller IS queries the base station table I3 in the signal processor database 26 to determine the status and availability of any neighboring base stations, including those base stations of other (MRS in the area. The definition of neighboring base stations include not only those within a provisionable “hop" based on the cell design reuse factor, but also includes, in the case of CDMA, results from remaining set information autonomously queried to mobile stations. with results stored in the base station table. Remaining set information indicates that mobile stations can detect other base station (sector) pilot channels which may exceed the “hop” distance, yet are nevertheless candidate base stations (or sectors) for wireless location purposes. Although cellular and digital cell design may vary, “hop” distance is usually one or two cell coverage areas away from the primary base station's cell coverage area. Having determined a likely set of base stations which may both detect the mobile station's transmitter signal, as well as to determine the set of likely pilot channels (i.e.. base stations and their associated physical antenna sectors) detectable by the mobile station in the area surrounding the primary base station (sector), the controller l5 initiates messages to both the mobile station and appropriate base stations (sectors) to perform signal measurements and to return the results of such measurements to the signal processing system regarding the mobile station to be located. This step may be accomplished via several interface means. ln a first case the controller l5 utilizes, for a given MSE, predetermined storage information in the NSC table 3l to determine which type of commands. such as man-machine or OSl commands are needed to request such signal measrurements for a given NSC. The controller generates the mobile and base station signal measurement commands appropriate for the MSC and passes the commands via the input queue 7 and the locations application programming interface in fig.l, to the appropriate MSC, using the authorized communications port mentioned earlier. In a second case the controller l5 communicates directly with base stations within having to interface directly with the MSC for signal measurement extraction. Upon receipt of the signal measurements, the signal classilier9 in Fig. 30 examines location application programming interface-provided message header information from the source of the location measurement (for example, from a fixed BS l22. a 34 IO 15 20 25 30 CA 02265875 1999-03-09 W0 98,1030-, PCT/US97Il5892 mobile station I40. a distributed antenna system I68 in Fig. I or message location data related to a home base station), provided by the location applications programming interface (L-Al’l) via the input queue 7 in Fig. 30 and determines whether or not device lilters I7 or image filters I‘) are needed. and assesses a relative priority in processing, such as an emergency versus a background location task. in terms of grouping like data associated with a given location request. In the case where multiple signal measurement requests are outstanding for various base stations, some of which may be associated with a different (MRS network, and additional signal classifier function includes sorting and associating the appropriate incoming signal measurements together such that the digital signal processor l7 processes related measurements in order to build ensemble data sets. Such ensembles allow for a variety of functions such as averaging, outlier removal over a timeperiod. and related filtering functions, and further prevent association errors from occuring in location estimate processing. Another function of the signal classifier/low pass filter component 9 is to filter information that is not useable, or information that could introduce noise or the effect of noise in the location estimate modules. Consequently low pass matching filters are used to match the in-common signal processing components to the characteristics of the incoming signals. low pass lilters match: Mobile Station. base station. CMRS and NSC characteristics, as wall as to classify Home Base Station messages. The signal processing subsystemcontains a base station database table l3 (Fig. 30) which captures the maximum number of CDMA delay spread fingers for a given base station. The base station identification code, or CLll or common language level identification code is useful in identifying or relating a human-labeled name descriptor to the Base Station. Latitude, longitude and elevation values are used by other subsystems in the location system for calibration and estimation purposes. As base stations and/or receiver characteristics are added. deleted, or changed with respect to the networlt used for location purposes, this database table must be modified to reflect the current network configuration. Just as an upgraded base station may detect additional CDMA delay spread signals, newer or modified mobile stations may detect additional pilot channels or (DNA delay spread fingers. Additionally different makes and models of mobile stations may acquire improved receiver sensitivities, suggesting a greater coverage capability. The table below establishes the relationships among various mobile station equipment suppliers and certain technical data relevant to this location invention. Although not strictly necessary, The MIN can be populated in this table from the PCS Service Provider's Customer Care system during subscriber activation and fulfillment, and could be changed at deactivation, or anytime the end-user changes mobile stations. Alternatively, since the lllll, manufacturer. model number, and software revision level information is available during a telephone call, this information could extracted during the call, and the remaining fields populated dynamically, based on manufacturer's’ specifications information previously stored in the signal processing subsystem 20. Default values are used in cases where the lllN is not found, or where certain information must be estimated. A low pass mobile station filter, contained within the signal classifier/low pass filter 9 of the signal processing subsystem 20, uses the above table data to perform the following functions: I) act as a low pass filter to adjust the nominal assumptions related to the maximum number of CDMA fingers, pilots detectable; and 2) to determine the transmit power class and the receiver thermal noise floor. Given the detected reverse path signal strength, the required value of SllSS,,5_ a corrected indication of the effective path 35 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 loss in the reverse direction (mobile station to BS), can be calculated based data contained within the mobile station table I I, stored in the signal processing database 26. The effects of the maximum Number of CDMA fingers allowed and the maximum number of pilot channels allowed essentially form a low pass filter effect, wherein the least common denominator of characteristics are used to filter the incoming RF signal measurements such that a one for one matching occurs. The effect of the transmit power class and receiver thermal noise floor values is to normalize the characteristics of the incoming lli signals with respect to those Ill signals used. The signal classifier/filter 20 is in communication with both the input queue 7 and the signal processing database 26. In the early stage of a location request the signal processing subsystem I42 in Fig. 4, will receive the initiating location request from either an autonomous 9-I-I notification message from a given IISC, or from a location application (for example, see Fig. 36), for which mobile station characteristics about the target mobile station I40 (Fig. I) is required. Referring to Fig. 30, a query is made from the signal processing controller I5 to the signal processning database 26, specifically the mobile station table II, to determine if the mobile station characteristics associated with the MIN to be located is available in table II. if the data exists then there is no need for the controller IS to query the wireless network in order to determine the mobile station characteristics, thus avoiding additional real-time processing which would otherwise be required across the air interface, in order to determine the mobile station MIN characteristics. The resulting mobile station information my be provided either via the signal processing database 26 or alternatively a query may be performed directly from the signal processing subsystem 20 to the MSC in order to determine the mobile station characteristics. Referring now to fig. 3|, a location application programming interface, I-API-(CS I39 to the appropriate Clflls customer care system provides the mechanism to populate and update the mobile station table II within the database 26. The L-API-(CS I39 contains its own set of separate input and output queues or similar implementations and security controls to ensure that provisioning data is not sent to the incorrect (MRS. and that a given (MRS cannot access any other Cl‘lllS’ data. The interface I I553 to the customer care system for Clllls-A llS0a provides an autonomous or periodic notification and response application layer protocol type, consisting of add, delete, change and verily message functions in order to update the mobile station table II within the signal processing database 26, via the controller IS. A similar interface l|S5b is used to enable provisioning updates to be received from (MRS-B customer care system I l50b. Although the L-API-(CS application message set may be any protocol type which supports the autonomous notification message with positive acknowledgment type, the TI lll.5 group within the American National Standards Institute has defined a good starting point in which the l-APT-CCS could be implemented, using the robust 0Sl Tllll X-interface at the service management layer. The object model defined in Standards proposal number TIl‘ll.5/96-22li9, Operations Administration, Maintenance, and Provisioning (0AI1&P) - Model for Interface Across Jurisdictional Boundaries to Support Electronic Access Service Ordering: Inquiry function, can be extended to support the L-API-CCS information elements as required and further discussed below. Other choices in which the L- API-CCS application message set may be implemented include ASCII, binary, or any encrypted message set encoding using the Internet protocols, such as TCP/IP, simple network management protocol, http, https, and email protocols. 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTlUS97/ 15892 Referring to the digital signal processor (DSP) I7, in communication with the signal classifier/ll’ filter 9. the DSP l7 provides a time series expansion method to convert non-HBS data from a format of an signal measure data ensemble of time-series based radio frequency data measurements. collected as discrete time-slice samples, to a three dimensional matrix location data value image representation. Other techniques further filter the resultant image in order to furnish a less noisy training and actual data sample to the location estimate modules. Alter I28 samples (In one embodiment) of data are collected of the delay spread-relative signal strength Ill data measurement sample: mobile station RX for _BS-l and grouped into a quantization matrix. where rows constitute relative signal strength intervals and columns define delay intervals. As each measurement row, column pair (which could be represented as a complex number or Cartesian poim pair) is added to their respective values to generate a Z direction of frequency of recurring measurement value pairs or a density recurrence lunction. By next applying a grid function to each x. y, and 2 value. a three- dimensional surface grid is generated. which represents a location data value or unique print of that I28-sample measurement. In the general case where a mobile station is located in an environment with varied clutter patterns, such as terrain undulations, unique man-made structure geometries (thus creating varied multipath signal behaviors). such as a city or suburb. although the first CDMA delay spread finger may be the same value for a fixed distance between the mobile station and BS antennas. as the mobile station moves across such an arc, different finger-data are measured. In the right image for the defined BS antenna sector, location classes, or squares numbered one through seven, are shown across a particular range of line of position (LOP). A traditional TOA/TDOA ranging method between a given BS and mobile station only provides a range along the arc, thus introducing ambiguity error. However a unique three dimensional image can be used in this method to specifically identify, with recurring probability, a particular unique location class along the same Line Of Position, as long as the multipath is unique by position but generally repeatable, thus establishing a method of not only ranging, but also of complete latitude, longitude location estimation in a Cartesian space. In other words, the unique shape of the "mountain image" enables a correspondence to a given unique location class along a line of position, thereby eliminating traditional ambiguity error. Although man-made external sources of interference. llayleigh fades, adjacent and co-channel interference, and variable clutter, such as moving traffic introduce unpredictability (thus no “mountain image” would ever be exactly alike), three basic types of filtering methods can be used to reduce matching/comparison error from a training case to a location request case: i.) select only the strongest signals from the forward path (ES to mobile station) and reverse path (mobile station to BS), 2.) Convolute the forward path in sample image with the reverse path in sample image, and 3.) process all image samples through various digital image filters to discard noise components. In one embodiment, convolution of forward and reverse images is performed to drive out noise. This is one embodiment that essentially nulls noise completely, even if strong and recurring, as long as that same noise characteristic does not occur in the opposite path. The third embodiment or technique of processing CDMA delay spread profile images through various digital image filters. provides a resultant “image enhancement" in the sense of providing a more stable pattern recognition paradigm to the neural net 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97ll5892 location estimate model. Forexample, image histogram equalization can be used to rearrange the images’ intensity values. or density recurrence values, so that the image's cumulative histogram is approximately linear. Other methods which can be used to compensate for a concentrated histogram include: I) Input Cropping, 2) Output Cropping and 3) Gamma Correction. Equalization and input cropping can provide particularly striking benefits to a CDMA delay spread profile image. Input cropping removes a large percentage of random signal characteristics that are non-recurring. Other filters and/or filter combinations can be used to help distinguish between stationary and variable clutter affecting multipath signals. for example, it is desirable to reject multipath fingers associated with variable clutter. since over a period of a few minutes such fingers would not likely recur. Further filtering can be used to remove recurring (at least during the sample period), and possibly strong but narrow “pencils” of ill‘ energy. A narrow pencil image component could be represented by a near perfect reflective surface, such as a nearby metal panel truck stopped at a traffic light. On the other hand. stationary clutter objects, such as concrete and glass building surfaces, adsorb some radiation before continuing with a reflected ray at some delay. Such stationary clutter-affected CDHA fingers are more likely to pass a 4X4 neighbor Median filter as well as a 40 to 50 percent Input Crop filter, and are thus more suited to neural net pattern recognition.. However when subjected to a 4X4 neighbor Median filter and 40 percent clipping, pencil-shaped fingers are deleted. Other combinations include. for example, a 50 percent cropping and 4X4 neighbor median filtering. Other filtering methods include custom linear filtering, adaptive (Weiner) filtering, and custom nonlinear filtering. The DSP I7 may provide data emsemble results, such as extracting the shortest time delay with a detectable relative signal strength, to the router/distributor 23, or alternatively results may be processed via one or more image filters l9, with subsequent transmission to the router/distributor 23. The router/distributor 23 examines the processed message data from the DSP I7 and stores routing and distribution information in the message header. The router/distributor 23 then forwards the data messages to the output queue 2f, for subsequent queuing then transmission to the appropriate location estimator F0lls. LOCATION CENTER HIGH LEVEL FUNCTIONALITY At a very high level the location center I42 computes location estimates for a wireless Mobile Station I40 (denoted the “target MS" or “MS") by performing the following steps: (23.l) more wireless infrastructure base stations I22; receiving signal transmission characteristics of communications communicated between the target MS I40 and one or (23.2) filtering the received signal transmission characteristics (by a signal processing subsystem I220 illustrated in fig. 5) as needed so that target MS location data can be generated that is uniform and consistent with location data generated from other target llSs I40. In particular, such uniformity and consistency is both in terms of data structures and interpretation of signal characteristic values provided by the NS location data; 10 20 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 (133) or folds, and labeled collectively as I224 in fig. 5), so that each such model may use the input target MS location data lor generating inputting the generated target HS location data to one or more MS location estimating models (denoted first order models a “location hypothesis" providing an estimate of the location of the target HS I40; (23.4) providing the generated location hypotheses to an hypothesis evaluation module (denoted the hypothesis evaluator I228 in Fig. 5): (a) for adjusting at least one of the target MS location estimates of the generated location hypotheses and related confidence values indicating the confidence given to each location estimate, wherein such adjusting uses archival information related to the accuracy of previously generated location hypotheses, (b) for evaluating the location hypotheses according to various heuristics related to, for example, the radio coverage area I20 terrain, the laws of physics, characteristics of likely movement of the target MS MO; and (c) for determining a most likely location area for the target MS MO. wherein the measurement of confidence associated with each input MS location area estimate is used for determining a "most likely location area"; and (23.5) outputting a most likely target MS location estimate to one or more applications I232 (fig. 2.0) requesting an estimate of the location of the target MS I40. Location Hypothesis Data Representation In order to describe how the steps (23.l) through (23.5) are performed in the sections below, some introductory remarks related to the data denoted above as location hypotheses will be helplul. Additionally. it will also be helpful to provide introductory remarks related to historical location data and the data base management programs associated therewith. for each target MS location estimate generated and utilized by the present invention, the location estimate is provided in a data structure (or object class) denoted as a "location hypothesis“ (illustrated in Table Lll-l). Although brief descriptions of the data fields for a location hypothesis is provided in the Table [H-l. many fields require additional explanation. Accordingly, location hypothesis data fields are further described as noted below. Table LH-I T0l1_lD first order model ID (providing this Location Hypothesis); note, since it is possible for location hypotheses to be generated by other than the F0l‘ls I224, in general, this field identifies the module that generated this location hypothesis. MS_|D The identification of the target MS I40 to this location hypothesis applies. pt_est The most likely location point estimate of the target MS I40. valid_pt Boolean indicating the validity of “pt_est”. CA 02265875 1999-03-09 W0 98/10307 PCT /US97/ 15892 area est Location Area Estimate of the target MS I40 provided by the FOM. ibis area estimate will be used whenever “image_area” below is NULL. valid_area Boolean indicating the validity of “area_est" (one of “pt_est” and “area_est" must be valid). adjust Boolean (true if adjustments to the fields of this location hypothesis are to be performed in the Context adjuster Module). pt_covering Reference to a substantially minimal area (e.g., mesh cell) covering of “pt_est". Note, since this MS I40 may be substantially on a cell boundary, this covering may, in some cases, include more than one cell. image_area Reference to a substantially minimal area (e.g., mesh cell) covering of “pt__covering” (see detailed description of the function, “confidence_adiuster"). Note that if this field is not NULL. then this is the target MS location estimate used by the location center l42 instead of "area_est". extrapolation_area llelerence to (if non-NULL) an extrapolated MS target estimate area provided by the location extrapolator submodule I432 ol the hypothesis analyzer l332. lhat is. this field, il non-NULL, is an extrapolation ol the “image_area” lield if it exists, otherwise this field is an extrapolation ol the “area_est" field. Note other extrapolation lields may also be provided depending on the embodiment ol the present invention, such as an extrapolation of the “pt_covering". conlidence A real value in the range [-1Ø + |.0] indicating a likelihood that the target MS MO is in (or out) ol a particulararea. ll positive: if “image__area" exists, then this is a measure ol the likelihood that the target MS MO is within the area represented by "image_area”. or if "image_area” has not been computed (e.g.. “adiust" is FALSE), then “area__est" must be valid and this is a measure oi the likelihood that the target MS I40 is within the area represented by "area_est". ll negative. then "area_est" must be valid and this is a measure ol the likelihood that the target MS I40 is NOT in the area represented by “area_est". ll it is zero (near zero), then the likelihood is unknown. 0riginal_limestarnp Date and time that the location signature cluster (delined hereinbelow) lor this location hypothesis was received by the signal processing subsystem I220. Active_Tirnestamp liun-time lield providing the time to which this location hypothesis has had its MS location 40 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 estimate(s) extrapolated (in the location extrapolator I432 of the hypothesis analyzer I332). Note that this field is initialized with the value from the “0riginal_Timestamp" field. Processing Tags and environmental for indicating particular types of environmental classifications not readily determined by the categorizations “0riginal_limestamp" field (e.g., weather, traffic). and restrictions on location hypothesis processing. loc_sig_cluster Provides access to the collection of location signature signal characteristics derived from communications between the target MS l40 and the base station(s) detected by this MS (discussed in detail hereinbelow); in particular, the location data accessed here is provided to the first order models by the signal processing subsystem I220; i.e., access to the “loc sigs" (received at “timestamp” regarding the location of the target MS) descriptor Original descriptor (from the first order model indicating why/how the Location Area Estimate and Confidence Value were determined). As can be seen in the Table LH-I, each location hypothesis data structure includes at least one measurement, denoted hereinafter as a confidence value (or simply confidence), that is a measurement of the perceived likelihood that an MS location estimate in the location hypothesis is an accurate location estimate of the target MS I 40. Since such confidence values are an important aspect of the present invention, much of the description and use of such confidence values are described below; however, a brief description is provided here. Each such confidence value is in the range -|.0 to l.0, wherein the larger the value. the greater the perceived likelihood that the target MS MO is in (or at) a corresponding MS location estimate of the location hypothesis to which the confidence value applies. As an aside, note that a location hypothesis may have more than one MS location estimate (as will be discussed in detail below) and the confidence value will typically only correspond or apply to one of the HS location estimates in the location hypothesis. Further, values for the confidence value field may be interpreted as: (a) -l.0 may be interpreted to mean that the target MS I40 is NOT in such a corresponding MS area estimate of the location hypothesis area, (b) 0 may be interpreted to mean that it is unknown as to the likelihood of whether the MS I40 in the corresponding MS area estimate, and (c) + |.0 may be interpreted to mean that the MS I40 is perceived to positively be in the corresponding MS area estimate. Additionally, note that it is within the scope of the present invention that the location hypothesis data structure may also include other related “perception" measurements related to a likelihood of the target MS I40 being in a particular MS location area estimate. for example, it is within the scope of the present invention to also utilize measurements such as, (a) "sufficiency factors" for indicating the likelihood that an MS location estimate of a location hypothesis is sufficient for locating the target MS I40; (b) “necessity lactors” for indicating the necessity that the target MS be in an particular area estimate. However, to more easily describe the present invention, a single confidence field is used having the interpretation given above. 4| Us 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/ 15892 Additionally, in utiliaing location hypotheses in, for example, the location evaluator l228 as in (23.4) above, it is important to keep in mind that each location hypothesis confidence value is a relative measurement. That is, for confidences, cf, and cf,, if cf . < = cf}, then for a location hypotheses H, and H, having cf, and cf,, respectively, the target MS I40 is expected to more likely reside in a target MS estimate of H, than a target HS estimate of H ,. Moreover, if an area, A, is such that it is included in a plurality of location hypothesis target MS estimates, then a confidence score, 6,, can be assigned to A, wherein the confidence score for such an area is a function of the confidences (both positive and negative) for all the location hypotheses whose (most pertinent) target MS location estimates contain A. That is, in order to determine a most likely target MS location area estimate for outputting from the location center W, a confidence score is determined for areas within the location center service area. More particularly, if a function. "f", is a function of the confidence(s) of location hypotheses, and f is a monotonic function in its parameters and f(cf,, cf}, cf,, ..., cl") = CS, for confidences cf-, of location hypotheses ll,‘ i= l,2,_.,N, with CS, contained in the area estimate for H,, then “f” is denoted a confidence score function. Accordingly, there are many embodiments for a confidence score function f that may be utilized in computing confidence scores with the present invention; e.g., (a) f(cf.. cf,, ,cf,,) = S cl, = CS); (b) f(cf,, cl,, ... , cl") = S cl," = C5,, n = l, 3, 5, ,.; (c) f(cf.,cf,,... ,d,,) = S(l(, * cl) = CS, . wherein |(,,i = I, 2, mare positive system (tunable) constants (possibly dependent on environmental characteristics such as topography, time, date, traffic. weather, and/or the type of base station(s) l22 from which location signatures with the target MS I40 are being generated. etc.). for the present description of the invention, the function f as defined in (c) immediately above is utilized. However, for obtaining a general understanding of the present invention. the simpler confidence score function of (a) may be more useful. It is important to note, though, that it is within the scope of the present invention to use other functions for the confidence score function. Coverage Area: Area Types And Their Determination The notion of "area type" as related to wireless signal transmission characteristics has been used in many investigations of radio signal transmission characteristics. Some investigators, when investigating such signal characteristics of areas have used somewhat naive area classifications such as urban, suburban, rural, etc. However, it is desirable for the purposes of the present invention to have a more operational definition of area types that is more closely associated with wireless signal transmission behaviors. To describe embodiments of the an area type scheme used in the present invention, some introductory remarks are first provided. Note that the wireless signal transmission behavior for an area depends on at least the following criteria: (23.8.l) substantially invariant terrain characteristics {both natural and man-made) of the area; e.g., mountains, buildings, lakes, highways. bridges, building density; (23.8.2) time varying environmental characteristics (both natural and man-made) oi the area; e.g., foliage, traffic. weather, special events such as baseball games; 10 15 20 25 30 CA 02265875 1999-03-09 wo gs/10307 PCT/US97/15892 (23.83) wireless communication components or infrastructure in the area; e.g., the arrangement and signal communication characteristics of the base stations I22 in the area. further, the antenna characteristics at the base stations I22 may be important criteria. Accordingly, a description of wireless signal characteristics for determining area types could potentially include a characterization of wireless signaling attributes as they relate to each of the above criteria. Thus, an area type might be: hilly, treed, suburban. having no buildings above 50 feet, with base stations spaced apart by two miles. However, a categorization of area types is desired that is both more closely tied to the wireless signaling characteristics of the area, and is capable of being computed substantially automatically and repeatedly over time. Moreover, for a wireless location system, the primary wireless signaling characteristics for categorizing areas into at least minimally similar area types are: thermal noise and, more importantly, multipath characteristics (e.g., multi path lade and time delay). Focusing lor the moment on the multipath characteristics, it is believed that (23.B.l) and (23.8.3) immediately above are, in general, more imponant criteria for accurately locating an MS I40 than (23.8.2). That is, regarding (23.B.l), multipath tends to increase as the density of nearby vertical area changes increases. For example, multipath is particularly problematic where there is a high density of high rise buildings and/or where there are closely spaced geographic undulations. In both cases, the amount of change in vertical area per unit of area in a horizontal plane (for some horizontal reference plane) may be high. Regarding (23.83), the greater the density of base stations I22, the less problematic multipath may become in locating an MS I40. Moreover, the arrangement of the base stations I22 in the radio coverage area I20 in fig. 4 may affect the amount and severity of multipath. Accordingly, it would be desirable to have a method and system for straightforwardly determining area type classifications related to multipath. and in particular, multipath due to (23.8.l) and (23.83). The present invention provides such a detennination by utilizing a novel notion of area type, hereinafter denoted “transmission area type" (or, “(transmission) area type” when both a generic area type classification scheme and the transmission area type discussed hereinafter are intended) for classifying “similar" areas, wherein each transmission area type class or category is intended to describe an area having at least minimally similar wireless signal transmission characteristics. That is, the novel transmission area type scheme of the present invention is based on: (a) the terrain area classifications; e.g., the terrain of an area surrounding a target MS I40, (b) the configuration of base stations I22 in the radio coverage area I20, and (c) characterizations of the wireless signal transmission paths between a target MS I40 location and the base stations I22. In one embodiment of a method and system lor determining suth (transmission) area type approximations, a partition (denoted hereinafter as P0) is imposed upon the radio coverage area I20 for partitioning for radio coverage area into subareas, wherein each subarea is an estimate of an area having included MS I40 locations that are likely to have is at least a minimal amount of similarity in their wireless signaling characteristics. To obtain the partition P0 of the radio coverage area I20, the following steps are performed: (23.8.4.l) Panition the radio coverage area I20 into subareas, wherein in each subarea is: (a) connected, (b) variations in the lengths of chords sectioning the subarea through the centroid of the subarea are below a predetermined threshold, (c) the subarea has an area below a predetermined value. and (d) for most locations (e.g., within a first 43 CA 02265875 1999-03-09 wo 93/10307 PCTlUS97/ 15892 or second deviation) within the subarea whose wireless signaling characteristics have been verified. it is likely (e.g.. within a first or second deviation ) that an MS M0 at one of these locations will detect (forward transmission path) and/or will be detected (reverse transmission path) by a same collection of base stations I22. for example, in a CDMA context. a first such collection may be (for the forward transmission path) the active set of base stations l22, or, the union of the active and candidate sets, or, the union of the active. candidate and/or remaining sets of base stations I22 detected by “most" HSs I40 in . Additionally (or alternatively), a second such collection maybe the base stations I22 that are expected to detect llSs M0 at locations within the subarea. Of course, the union or intersection ol the first and second collections is also within the scope of the present invention for partitioning the radio coverage area I20 according to (d) above. it is worth noting that it is believed that base station I22 power levels will be substantially constant. However, even if this is not the case, one or more collections for (d) above may be determined empirically and/or by computationally simulating the power output of each base station l22 at a predetermined level. Moreover, it is also worth mentioning that this step is relatively straightforward to implement using the data stored in the location signature data base I320 (i.e., the verified location signature clusters discussed in detail hereinbelow). Denote the resulting partition here as P,. (23.8.41) Partition the radio coverage area I20 into subareas, wherein each subarea appears to have substantially homogeneous terrain characteristics. Note. this may be performed periodically substantially automatically by scanning radio coverage area images obtained from aerial or satellite imaging. for example, Earthwatch Inc. of Longmont, CO can provide geographic with 3 meter resolution from satellite imaging data. Denote the resulting partition here as P,. (23.8.43) Overlay both of the above partitions of the radio coverage area I20 to obtain new subareas that are intersections of the subareas from each of the above partitions. This new partition is Po (i.e., P0 = P, intersect P,), and the subareas of it are denoted as “Po subareas". Now assuming Po has been obtained, the subareas of P0 are provided with a lirst classification or categorization as follows: (23.ll.4.4) Determine an area type categorization scheme for the subareas of P,. For example, a subarea, A, of P,, may be categorized or labeled according to the number of base stations l22 in each of the collections used in (23.8.4.|)(d) above for determining subareas ol P,. Thus, in one such categorization scheme. each category may correspond to a single numberx (such as 3), wherein for a subarea, A. of this category, there is a group of x (e.g., three) base stations I22 that are expected to be detected by a most target llSs M0 in the area A. Other embodiments are also possible, such as a categorization scheme wherein each category may correspond to a triple: of numbers such as (5, 2, I), wherein lot a subarea A of this category, there is a common group of 5 base stations I22 with two-way signal detection expected with most locations (e.g., within a first or second deviation) within A. there are 2 base stations that are expected to be detected by a target MS MO in A but these base stations can not detect the target MS, and there is one base station I22 that is expected to be able to detect a target MS in A but not be detected. 44 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 (23.8.45) Determine an area type categorization scheme for the subareas of P3. Note that the subareas of P2 may be categorized according to their similarities. ln one embodiment, such categories may be somewhat similar to the naive area types mentioned above (e.g. dense urban, urban, suburban, rural, mountain, etc.). However, it is also an aspect of the present invention that more precise categorizations may be used, such as a category for all areas having between 20,000 and 30,000 square feet of vertical area change per ll.000 square feet of horizontal area and also having a high traffic volume (such a category likely corresponding to a “moderately dense urban” area tree)- (23.8.4.6) Categorize subareas of P0 with a categorization scheme denoted the “PD categorization," wherein for each Po subarea. A, of P9 a “P0 area type” is determined for A according to the following substep(s): (a) Categorize A by the two categories from (23.8.-1.4) and (23Ø5) with which it is identified. fhus, A is categorized (in a corresponding Po area type) both according to its terrain and the base station infrastructure configuration in the radio coverage area I20. (23.8.41) For each Po subarea, A, of P0 perform the following step(s): (a) Determine a centroid, C(A), for A; (b) Detennine an approximation to a wireless transmission path between C(A) and each base station l22 of a predetermined group of base stations expected to be in (one and/or two-way) signal communication with most target MS MO locations in A. For example, one such approximation is a straight line between C(A) and each of the base stations I22 in the group. However. other such approximations are within the scope of the present invention, such as, a generally triangular shaped area as the transmission path, wherein a first vertex of this area is at the corresponding base station for the transmission path, and the sides of the generally triangular shaped defining the first vertex have a smallest angle between them that allows A to be completely between these sides. (c) for each base station I22. B3,, in the group mentioned in (b) above, create an empty list, BS,-list, and put on this list at least the Po area types for the “significant” Po subareas crossed by the transmission path between C(A) and BS«,. Note that “significant” Po subareas may be defined as, for example, the Po subareas through which at least a minimal length of the transmission path traverses. Alternatively, such "significant" Po subareas may be defined as those Pu subareas that additionally are know or expected to generate substantial multipath. (d) Assign as the transmission area type for A as the collection of BS,-lists. Thus, any other Po subarea having the same (or substantially similar) collection of lists of P0 area types will be viewed as having approximately the same radio transmission characteristics. Note that other transmission signal characteristics may be incorporated into the transmission area types. for example, thennal noise characteristics may be included by providing a third radio coverage area I20 partition, P3, in addition to the partitions of P. and P7 generated in (23.8.4.|) and (23.fl.4.2) respectively. Moreover, the time varying characteristics of (23.8.2) maybe 45 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 incorporated in the transmission area type frame work by generating multiple versions of the transmission area types such that the transmission area type for a given subarea of P0 may change depending on the combination of time varying environmental characteristics to be considered in the transmission area types. for instance. to account for seasonality, four versions ol the partitions P, and P, may be generated. one for each of the seasons, and subsequently generate a (potentially) different partition Pa for each season. Further. the type and/or characteristics of base station l22 antennas may also be included in an embodiment of the transmission area type. Accordingly, in one embodiment ol the present invention, whenever the term “area type" is used hereinbelow. transmission area types as described hereinabove are intended. Location lnlormation Data Bases And Data location Data Bases Introduction It is an aspect of the present invention that MS location pmcessing perlomred by the location cemer l42 should become increasingly better at locating a target MS I40 both by (a) building an increasingly more detailed model of the signal characteristics of locations in the service area for the present invention, and also (b) by providing capabilities for the location center processing to adapt to environmental changes. One way these aspects of the present invention are realized is by providing one or more data base management systems and data bases for: (a) storing and associating wireless MS signal characteristics with known locations ol l1Ss I40 used in providing the signal Such stored associations may not only provide an increasingly better model of the signal characteristics of the geography of the service area, but also provide an increasingly better model of more changeable signal characteristic aflecting environmental factors such as weather, seasons, and/or traffic patterns; (b) adaptively updating the signal characteristic data stored so that it reflects changes in the envimrrment of the service area such as, for example, a new high rise building or a new highway. llelerring again to Fig. 5 ol the collective representation of these data bases is the location information data bases I232. Included among these data bases is a data base for providing training and/or calibration data to one or more trainable/calibratable folls I224, as well as an archival data base for archiving historical MS location inlomration related to the performance of the l0Ms These data bases will be discussed as necessary hereinbelow. However, a further brief introduction to the archival data base is provided here. Accordingly. the term. “location signature data base” is used hereinafter to denote the archival data base and/or data base management system depending on the context of the discussion. The location signature data base (shown in, for example, Fig. 6 and labeled I320) is a repository for wireless signal characteristic data derived lmm wireless signal communications between an MS I40 and one or more base stations I22, wherein the corresponding location of the MS MO is lrnown and also stored in the location signature data base I320. More particularly, the location signature data base I320 associates each such known MS location with the wireless signal characteristic data derived from wireless signal communications between the MS I40 and one or more base stations I22 at this MS location. Accordingly, it is an aspect of the present invention 46 20 25 30 CA 02265875 1999-03-09 W0 9g/10307 PCT /US97/15892 to utilize such historical MS signal location data for enhancing the correctness and/or confidence of certain location hypotheses as will be described in detail in other sections below. Data Representations for the location Signature Data Base lhere are four fundamental entity types (or object classes in an object oriented programming paradigm) utilized in the location signature data base I320. Briefly, these data entities are described in the items (24.|) thmugh (24.4) that follow: (24!) (verified) location signatures: Eadi silch (verified) location signature describes the wireless signal characteristic measurements between a given base station (e.g., BS I22 or LBS I52) and an MS l40 at a (verified or ltnown) location associated with the (verified) location signature. That is. a verified location signature corresponds to a location whose coordinates such as latitude-longitilde coordinates are known, while simply a location signature may have a lcriown or unknown location corresporlding with it. Note that the term (verified) location signature is also denoted by the abbreviation, “(verified) loc sig" hereinbelow; (24.2) (verified) location signature clusters: Each such (verified) location signature cluster includes a collection of (verified) location signatures corresponding to all the location signatures between a target MS I40 at a (possibly verified) presumed substantially stationary location and each BS (e.g., in or I52) from which the target MS 140 can detect the BS's pilot channel gardless of the classification ol the BS in the target NS 6.9., for CDMA, regardless of whether a BS is in the MS's active, candidate or remaining base station sets, as one skilled in the art will understand). Note that for simplicity here, it is presumed that each location signature cluster has a single fixed primary base station to which the target MS I40 synchronizes orobtains its timing; (243) “composite location objects (or entities)”: Each such entity is a rrlore general entity than the verified location signature cluster. An object of this type is a collection of (verified) location signatures that are associated with the same MS I40 at substantially the same location at the same time and each such locsig is associated with a different base station. However, there is no requirement that a loc sig from each BS I22 for which the MS I40 can detect the BS’: pilot channel is included in the “composite location object (or entity)"; and (24.4) NS location estimation data that includes MS location estimates output by one or nlore MS location estimating first order models I224. such MS location estimate data is described in detail hereinbelow. It is imponant to note that a loc sig is. in one embodiment, an instance of the data structure containing the signal characteristic measurements output by the signal filtering and normaliring subsystem also denoted as the signal processing subsystem I220 describing the signals between: (i) a specific base station I22 (BS) and (ii) a mobile station I40 (MS), wherein the BS's location is ltnown and the llS's location is assumed to be substantially constant (during a 2-5 second interval in one embodiment of the present invention), during communication with the HS I40 lor obtaining a single instance ol loc sig data. although the HS location may or may not be known. further, for notational purposes. the BS I22 and the MS I40 for a loc sig hereinafter will be denoted the “BS associated with the loc sig", and the “MS associated with the loc sig" respectively. Moreover, the location oi the MS I40 at the time the loc sig data is obtained will be denoted the "location associated with the loc sig" (this location possibly being unknown). In particular, for each (verified) loc sig includes the following: (25.1) llS_type: the make and model of the target MS I40 associated with a location signature instantiation; note that the type of MS I40 can also be derived lmm this entry; e.g.. whether MS I40 is a handset llS,car-setl1S, oran NS for location only. Note as all aside. for at least (DNA, the type of MS I40 pmvides information as to the number of fingers that may be measured by the llS., as one skilled in the will appreciate. 47 CA 02265875 1999-03-09 wo 93/10307 PCT/US97I15892 (25.2) BS_icl: an identification of the base station I22 (or. location base station I52) communicating with the target MS; (253) MS_|oc: a representation of a geographic location (eg. latitude-longitude) or area representing a verified/known MS location where signal characteristics between the associated (location) base station and MS I40 were received. That is, if the “verified_flag” attribute (discussed below) is TRUE, then this attribute includes an estimated location of the target MS. ll verified_flag is FALSE, then this attribute has a value indicating “location unknown”. llote “llS_Ioc” may include the following two subfields: an area within which the target MS is presumed to be, and a point location (e.g., a latitude and longitude pair) where the target MS is presumed to be (in one embodiment this is the centroid of the area); (25.4) verified_flag: a flag for determining whether the loc sig has been verified; ie., the value here is TRUE ill a location of llS_loc has been verified, FALSE otherwise. Note, if this field is TRUE (i.e., the loc sig is verified), then the base station identified by BS__id is the current primary base station for the target NS; (25.5) confidence: a value indicating how consistent this loc sig is with other loc sigs in the location signature data base I320; the value for this entry is in the range [0, I] with 0 corresponding to the lowest (i.e., no) confidence and I corresponding to the highest confidence. That is, the confidence factor is used for determining how consistent the loc sig is with other “similar” verified loc sigs in the location signature data base l320,wherein the greater the confidence value. the better the consistency with other loc sigs in the data base. Note that similarity in this context may be operationalized by at least designating a geographic proximity of a loc sig in which to determine if it is similar to other loc sigs in this designated geographic proximity and/or area type (e.g., transmission area type as elsewhere herein). Thus, environmental characteristics may also be used in detennining similarities such as similar time of occurrence (e.g., of day, and/or of rrronth), similar weather (e.g., snowing, raining, etc.). Note, these latter characteristics are different fmm the notion of geographic proximity since proximity may be only a distance measurement about a location. Note also that a loc sig having a confidence factorvalue below a predetennined threshold may not be used in evaluating MS location hypotheses generated by the l0lls I224. (25.6) timestamp: the time and date the loc sig was received by the associated base station of BS_id; (25.7) signal topography characteristics: In one embodiment, the signal topography characteristics retained can be represented as characteristics of at least a two-dimensional generated surface. That is. such a surface is generated by the signal processing subsystem I220 from signal characteristics accumulated over (a relatively short) time interval. for example, in the two-dimensional surface case, the dimensions for the generated surface may be, lorexample, signal strength and time delay. That is, the accumulations over a brief time interval of signal characteristic measurements between the BS I22 and the MS I40 (associated with the loc sig) may be classified according to the two signal characteristicdimensions (e.g., signal strength and conesponding time delay). That is, by sampling the signal characteristics and classifying the samples according to a mesh of discrete cells or bins, wherein each cell conespondi to a different range of signal strengths and time delays a tally of the number of samples falling in the range of each cell can be maintained. Accordingly, for each cell, its corresponding tally may be interpreted as height of the cell, so that when the heights of all cells are considered, an undulating or mountainous surface is provided. In particular, fora cell mesh of appropriate fineness, the “mountainous surface", is believed to, under most circumstances, provide a contour that is substantially 48 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT /U S97! 15892 unique to the location of the target MS I40. Note that in orte embodiment, the signal samples are typically obtained throughout a predetermined signal sampling time interval of 2-5 seconds as is discussed elsewhere in this specification. ln particular, the signal topography characteristics retained fora loc sig include certain topographial characteristics of such a generated mountainous surface. For example, each loc sig may include: for each local maximum (of the loc sig surface) above a predetennined noise ceiling threshold, the (signal strength, time delay) coordinates of the cell of the local maximum and the conesponding height of the local maximum. Additionally, certain gradients may also be inclrrded for characterizing the “steepness" of the surface mountains. Moreover, note that in some embodiments, a frequency may also be associated with each local maximum. Thus, the data retained for each selected local maximum can include a quadruple of signal strength, time delay, height and frequency. further note that the data types here may vary. However, for simplicity, in parts of the description of loc sig processing related to the signal characteristics here, it is assumed that the signal characteristic topography data structure here is a vector, (25.8) quality__obj: signal quality (or error) measurements, e.g, Eb/No values, as one skilled in the art will understand; (25.9) noise_ceiling: noise ceiling values used in the initial filtering of noise from the signal topography characteristics as provided by the signal processing subsystem I220; (25.l0) power_level: power levels of the base station (e.g, l22 or I52) and MS M0 for the signal measurements; (25.l l) timing_error: an estimated (or maximum) timing error between the present (associated) BS (e.g., an infrastructure base station l22 or a location base station l52) detecting the target MS MO and the currem primary BS I22 forthe target MS MO. Note that if the BS I22 associated with the loc sig is the primary base station, then the value here will be zero; (2S.l2) c|uster_ptr: a pointer to the location signature composite entity to which this loc sig belongs (25.|3) repeatable: TRUE iff the loc sig is "repeatable” (as described hereinafter), FALSE otherwise. Note that each verified loc sig is designated as either “repeatable" or “random”. A loc sig is repeatable if the (verified/known) location associated with the loc sig is such that signal characteristic measurements between the associated BS l22 and this MS can be either replaced at periodic time intervals, or updated substantially on demand by most recent signal characteristic measurements between the associated base station and the associated MS I40 (or a comparable MS) at the verilied/known location. llepeatable loc sigs may be, for example, provided by stationary or fixed location l1Ss I40 (e.g., fixed location transceivers) distributed within certain areas of a geographical region serviced by the location center M2 for providing MS location estimates. That is, it is an aspect of the present invention that each such stationary HS 140 can be contacted by the location cemer I42 (via the base stations of the wireless infrastructure) at substantially any time for providing a new collection (i.e., cluster) of wireless signal characteristics to be associated with the verified location for the transceiver. Alternatively, repeatable loc sigs maybe obtained by, for example, obtaining location signal measurements manually fmm workers who regularly traverse a predetennined route through some portion of the radio coverage area; ie, postal workers (as will be described in more detail hereinbelow). A loc sig is random if the loc sig is not repeatable. llandom loc sigs are obtained, for example, from verifying a previously unknown target MS location once the MS I40 has been located. Such verifications may be accomplished by, for example, avehicle having one or more location verifying devices such as a GPS receiver and/or a manual location input capability becoming sufficiently close to the located target MS Ml) so that the location of the vehicle may be associated with the wireless signal characteristics of the MS 140. 49 10 20 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT /U S97! 15892 Vehicles having such location detection devices irray include: (a) vehicles that travel to locations that are priniarify for anotfrer purpose than to verify loc sigs, e.g.. police cars. ambulances. fire trudcs, rescue units. courier services and taxis; and/or (b) vehicles whose primary purpose is to verify loc sigs; e.g., location signal calibration vehicles Additionally, vehicles having both wireless transceivers and location verifying devices rrray provide the location center I42 with random loc sigs. Note, a repeatable loc sig may become a random loc sig if an MS I40 at the location associated with the loc sig becomes undetectable such as, for example, when the MS I40 is removed from its verified location anrl therefore the loc sig fortlre location can not be readily updated. Additionally, note that at least in one embodiment of the signal topography characteristics (25.7) above, such a first surface may be generated for the (forward) signals from the base station I22 to the target MS I40 and a second such surface my be generated for (or alternatively, the first surface may be enhanced by increasing its dimensionality with) the signals fmm the MS I40 to the base station I22 (denoted the reverse signals). Additionally, in some embodirrients the location hypothesis may include an estimated error as a irreasurement of perceived accuracy in addition to or as a substitute for the confidence field discussed hereinabove. Moreover, location hypotheses may also include a text field for providing a reason for the values of one or more of the location hypothesis fields. forexample, this text field may provide a reason as to why the confidence value is low, or provide an indication that the wireless signal measurements used had a low signal to noise ratio. loc sigs have the following functions or object rrrethods associated therewith: (26.l) A “nomialization" method for normalizing loc sig data according to the associated MS I40 and/or BS I22 signal processing and generating characteristics. That is. the signal processing subsystem l220,one embodiment being described in the PCT patent application titled, "Wireless location Using A Plurality of Commercial Network Infrastructures," by f. W. l.eBlanc and the present inventor(s), provides (methods for loc sig objects) for “nomializing" each loc sig so that variations in signal characteristics resulting from variations in (forexample) MS signal processing and generating characteristics of different types of MS‘: may he reduced. In particular. since wireless network designers are typically designing networks for effective use of hand set MS's I40 having a substantially common minimum set of performance characteristics, the nomialization methods provided here transform the loc sig data so that it appears as though the loc sig was provided by a common hand set MS I40. However, other methods may also be provided to “normalize" a loc sig so that it may be compared with loc sigsobtairied from other types of l1S's as well. Note that such normalization techniques include. for example. interpolating and extrapolating according to power levels so that loc sigs may be nomialized to the same power level for, o.g., comparison purposes Normalization for the BS I22 associated with a loc sig is similar to the nonnalization for MS signal processing and generating characteristics. Just as with the MS normalization, the signal processing subsystem I220 provides a loc sig method for “normalizing" loc sigs according to base station signal pmcessing and generating characteristics. Note. however, loc sigs stored in the location signature data base I320 are NOT "nonnalized" according to either MS or BS signal processing and generating characteristics. That is, "raw" values of the wireless signal characteristics are stored with each loc sig in the location signature data base I320. 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT /U S97/15892 (26.2) A method for determining the “area type” corresponding to the signal transmission characteristics of the area(s) between the associated 88 I22 and the associated MS MO location for the loc sig. Note, such an area type may be designated by, for example, the techniques for determining transmission area types as described hereinabove. (263) Other methods are conuemplated for determining additional environmental characteristics of the geographical area between the associated BS I22 and the associated MS I40 location for the loc sig; e.g, a noise value indicating the amount of noise likely in sudi an ama Referring now to the composite location objects and verified location signature clusters of (24.3) and (24.2) respectively, the following infomiation is contained in these aggregation objects: (27.I.l) an identification of the BS I22 designated asthe primary base station for communicating with the target MS I40; (27.l.2) a reference to each lot sig in the location signature data base I320 that is lorthe same MS location at substantially the same time with the primary BS as identified in (27.l); (27.l_3) an identification of each base station (e.g., I22 and I52) that can be deteaed by the MS I40 at the time the location signal measurements are obtained. Note that in one embodimem, each composite location object includes a bit string having a corresponding bit for each base station, wherein a “I” forsuch a bit indicates that the corresponding base station was identified by the MS, and a “0” indicates that the base station was not identified. In an alteniative embodiment, additional location signal measurements may also be included from other non-primary base stations forexample, the target MS I40 may communicate with other base stations than it's primary base station. However, since the timing for the I15 I40 is typically derived fmm it's primary base station and since timing synchronization between base stations is not exact (e.g.. in the case of CDMA, timing variations may be plus or minus I microsecond)at least some of the location signal measurements may be less reliable that the measurements from the primary base station, unless a forced handbll technique is used to eliminate system timing errors among relevant base stations; (27.l.4) a completeness designation that indicates whether any lot sigs for the composite location object have been removed fmm (or invalidated in) the location signature data base I320. Note, a verified composite location object is designated as “incomplete" ifa loc sig initially referenced by the verified composite location object is deleted from the location signature data base I320 (eg. because of a confidence that is too low). further note that if all loc sigs fora composite location object are deleted, then the composite object is also deleted from the location signature data base I320. Also note that common fields between loc sigs referenced by the same composite location object may be provided in the composite location object only (e.g., timestamp, etc). Accordingly, a composite location object that is complete (i.e.. not incomplete) is a verified location signature cluster as described in (242). Location Center Architecture 5| 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 l’CTIUS97/ 15892 Overview of Location Center functional Components Fig.5 presents a high level diagram of the location center I42 and the location engine I39 in the context of the infrastructure fortfre entire location system of tire present invemion. ft is important to note that the architecture for the location center I42 and the location engine I39 provided by the present invention is designed forextensibility and flexibility so that MS I40 location accuracy and reliability maybe enhanced as further location data become available and as enhanced MS location techniques become available. In addressing the design goals of extensibility and flexibility, the high level architecture for generating and processing MS location estimates may be considered as divided into the following high level functional groups described hereinbelow. low Level Wireless Signal Processing Subsystem for Receiving and Conditioning Wireless Signal Measurements A first functional group of location engine I39 modules is for performing signal processing and filtering of MS location signal data received from a conventional wireless (e.g. CDMA) infrastructure, as discussed in the steps (23.I) and (232) above. This group is denoted the signal processing subsystem I220 herein. One embodiment of such a subsystem is described in the PCT patent application titled, “Wireless Location Using A Plurality of Commercial Network Infrastructures," by F. W. felilanc and the present inventor(s). . Initial location Estimators: first Order Models A second functional group of location engine I39 modules is for generating various target MS I40 location initial estimates. as described in step (23.3). Accordingly, the modules here use input provided by the signal processing subsystem I220. This second functional group includes one or nrore signal analysis modules or models, each hereinafter denoted as a first order model I224 (POM), for generating location hypotheses fora target MS I40 to be located. Note that it is intended that each such f0M I224 use a different technique for detennining a location area estimate for the target MS I40. A brief description of some types of first order models is provided immediately below. Note that fig. 8 illustrates another, more detail view of the location system for the present invention. In particular, this figure illustrates some of the f0Ms I224 contemplated by the present invention, and additionally illustrates the primary communications with other modules of the location system for the present invemion. However, it is important to note that the present invention is not limited to the fOMs I224 shown and discussed herein. lhat is, it is a primary aspect of the present invention to easily incorporate f0Ms using other signal processing and/or computational location estimating techniques than those presented herein. Further, note that each f0M type may have a plurality of its models incorporated into an embodiment of the present invemion. For example, (as will be described in further detail below), one such type of model or FOM I224 (hereinafter models of this type are refened to as “distance models") may be based on a range or distance computation and/or on a base station signal reception angle determination between the target MS I40 from each of one or more base stations. Basically, such distance models I224 detennine a location estimate of the target MS I40 by detennining a distance offset from each of one or more base stations I22. possibly in a particular direction from each (some of) the base stations, so that an intersection of each area focus defined by the base station offsets may provide an estimate of the location of the target MS. Distance nrodel FOMs I224 may compute such offsets based on: 52 20 25 30 CA 02265875 1999-03-09 W0 93,1030-; PCT/US97/15892 (a) signal timing measurenrents between the target mobile station I40 and one or more base stations I22; e.g-, timing measurements such as time difference of arrival (T DOA), or time of arrival (T On). Note that both forward and reverse signal path timing measurements may be utilized; (b) signal strength measurements (e.g., relative to power control settings of the MS I40 and/orone or more BS l22); and/or (c) signal angle of arrival measurements. or ranges thereof. at one or more base stations I22 (such angles and/or angular ranges provided by. e.g.. base station antenna sectors having angular ranges of l20° or 60°, or, so called “SMART antennas" with variable angulartransmission ranges of 2° to I20‘). Accordingly, a distance model may utilize triangulation or trilateration to compute a loration hypothesis having either an area location or a point location for an estirrrate of the target MS I40. Additionally, in some embodiments location hypothesis may include an estimated enor Another type of f0l‘l l224 is a statistically based first order model l224, wherein a statistical technique. such as regression techniques (e.g., least squares, partial least squares, principle decomposition), or e.g., Bollenger Bands (e.g., for computing minimum and maximum base station offsets). In general, models of this type output location hypotheses determined by performing one or more statistical techniques or comparisons between the verified location signatures in location signature data base I320, and the wireless signal measurenrents fmm a target MS. Models of this type are also referred to hereinafter as a "stochastic signal (first order) model" or a "stochastic FOM” ora “statistical model" Still another type of FOM I 224 is an adaptive learning model. such as an artificial neural net or a genetic algorithm, wherein the FOM may be trained to recognize or associate each of a plurality of locations with a corresponding set of signal characteristics for communications between the target MS I40 (at the location) and the base stations I22. Moreover, typically such a FOM is expected to accurately interpolate/extrapolate target MS I40 location estimates from a set of signal characteristics from an unknown target MS I40 location. Models of this type are also referred to hereinafter variously as “artificial neural net models” or “neural net models" or “trainable models" or “learning models." Note that a related type of fOl1 I224 is based on pattern recognition. These l0l1s can recognize patterns in the signal characteristics of communications between the target MS I40 (at the location) and the base stations I22 and thereby estimate a location area of the target MS. However, such FOMs may not be trainable. Yet another type of FOM I224 can be based on a collection of dispersed low power, low cost fixed location wireless transceivers (also denoted "location base stations l52" hereinabove) that are pmvided for detecting a target MS l40 in areas where. e.gs there is insufficient base station I22 infrastructure coverage for providing a desired level of MS I40 location accuracy. forexample, it may uneconomical to provide high traffic wireless voice coverage of a typical wireless base station I22 in a nature preserve or at a fair ground that is only populated a few days out of the year. However, if such low cost location base stations I52 can be directed to activate and deactivate via the direction of a FOM I224 of the present type, then these location base stations can be used to both location a target MS MO and also provide indications of where the target MS is not. Forexample. if there are location base stations I52 populating an area where the target MS I40 is presumed to be, then by activating these location base stations I52. evidence may be obtained as to whether or not the target MS is actually in the area; e.g., if the target MS MO is detected by a location base station I52, then a corresponding location hypothesis having a location estimate corresponding to the coverage area of the location base station may have a very high confidence value. Alternatively. if the target MS MO is not detected by a location base station 10 20 25 30 CA 02265875 1999-03-09 wo 93,m3o7 PCTIUS97/15892 I52, then a corresponding location hypothesis having a location estimate conesponding to the coverage area of the location base station may have a very low confidence value. Models of this type are referred to hereinafter as “location base station riiodels." Yet another type of Hill I224 can be based on input fmm a mobile base station I48, wherein location hypotheses may be generated fmm target MS l40 location data received from the mobile base station M8. Still other types of FOM I224 can be based on various techniques for recognizing wireless signal measurement patterns and associating particular patterns with locations in the coverage area I20. for example, artificial neural networks or other learning models can used as the basis for various f0l‘ls. Note that the foil types mentioned here as well as other POM types are discussed in detail hereinbelow. Moreover, it is important to lieep in mind that a novel aspect of the present invention is the simultaneous use or activation of a potentially large number of such first order models 1224, wherein such f0l‘ls are not limited to those described herein. Thus, the present invemion provides a framework for incorporating MS location estimators to be subsequently provided as new f0ils in a straightforward manner. for example. a f0l‘l I224 based on wireless signal time delay measurements from a distributed antenna system for wireless communication may be incorporated into the present invemion for locating a target MS l40 in an enclosed area serviced by the distributed antenna system. Accordingly, by using such a distributed antenna foil, the present invention may determine the floor of a multi-story building from which a target MS is transmitting. Thus, MSs I40 can be located in three dimensions using such a distributed antenna FOM. Additionally, f0Hs for detecting certain registration changes within, for example, a public switched telephone network can also be used for locating a target MS M0. for example, for some MSs I40 there may be an associated or dedicated device for each such MS that allows the MS to lunction as a cordless phone to a line based telephone network when the device detects that the MS is within signaling range. In one use of such a device (also denoted herein as a “borne base station"), the device registers with a home location register of the public switched telephone network when there is a status change such as from not detecting the conesponding MS to detecting the MS, or visa versa, as one skilled in the art will understand. Accordingly, by providing a FOM that accesses the MS status in the home location register. the location engine I39 can determine whether the MS is within signaling range of the home base station or not. and generate location hypotheses accordingly. Moreover, other foils based on, for example, chaos theory and/or fractal theory are also within the scope of the present invention. It is important to note the following aspects of the present invention relating to f0lls I224: (23.l) Each such first order model I224 may be relatively easily incorporated into and/or removed from the present invention. for example, assuming that the signal processing subsystem I220 provides uniform input to the f0Ms, and there is a unilonn FOM output interface, it is believed that a large majority (if not substantially all) viable MS location estimation strategies may be accommodated. Thus, it is straightforward to add or delete such FOMs l 224. (281) Each such first order model I224 may be relatively simple and still provide significant MS I40 locating functionality and predictability. for example, much of what is believed to be comrrion or generic MS location processing has been coalesced into, for example: a location hypothesis evaluation subsystem, denoted the hypotheses evaluator I228 and described immediately below. lhus, the present invention is modular and extensible such that. lorexample, (and importantly) different first order models I224 may be utilized depending on the signal transmission characteristics of the geographic region serviced by an embodiment of the present invention. Thus, a simple configuration of the present invention may have a small number of i0Ms l224 lor a simple wireless signal environment (e.g., flat terrain. 54 20 25 30 CA 02265875 1999-03-09 W0 93/1030‘; PCT/US97/15892 no urban canyons and low population density). Alteniatively, for complex wireless signal environments such as in cities like San Francisco, Tokyo or New York, a large numberof FOMs I224 may be simultaneously utilized for generating HS location hypotheses. An lntroduction to an Evaluator for location Hypotheses: Hypothesis Evaluator A third functional gmup of location engine I39 modules evaluates location hypotheses output by the first order models i224 and thereby provides a “most likely” target MS location estimate. The modules for this functional group are collectively denoted the hypothesis evaluator I228. Hypothesis Evaluator lntroduction A primary purpose of the hypothesis evaluator l228 is to mitigate conflicts and ambiguities related to location hypotheses output by the first order models I224 and thereby output a “most likely" estimate of an MS forwhich there is a request for it to be located. In providing this capability, there are various related embodiments of the hypothesis evaluator that are within the scope of the present invention. Since each location hypothesis includes both an MS location area estiiriate and a corresponding confidence value indicating a perceived confidence or lilcelihood of the target MS being within the conesponding location area estimate. there is a monotonic relationship between MS location area estimates and confidence values. That is, by increasing an MS location area estimate, the corresponding confidence value may also be increased (in an extreme case, the location area estimate could be the entire coverage area I20 and thus the confidence value may likely correspond to the highest level of certainty Le, + l.0). Accordingly, given a target MS location area estimate (of a location hypothesis), an adjustment to its accuracy may be perfonired by adjusting the MS location area estimate and/or the corresponding confidence value. Thus, if the confidence value is, for example, excessively low then the area estirriate may be increased as a technique for increasing the confidence value. Alternatively, if the estirriated area is excessively large, and there is flexibility in the conesponding confidence value, then the estimated area may be decreased and the confidence value also decreased. Thus, if at some point in the processing of a location hypothesis, if the location hypothesis is judged to be more (less) accurate than initially determined. then (i) the confidence value of the location hypothesis can be increased (decreased), and/or (ii) the MS location area estimate can be decreased (increased). in a first class of embodimems, the hypothesis evaluator I228 evaluates location hypotheses and adjusts or modifies only their confidence values for MS location area estimates and subsequently uses these MS location estimates with the adjusted confidence values for detennining a “most likely" MS location estimate for outputting. Accordingly, the MS location area estimates are not substantially rriodified. Alternatively, in a second class of embodiments for the hypothesis evaluator l22B, HS location area estimates can be adjusted while confidence values remain substantially fixed. Of course, hybrids between the first two embodiments can also be provided. Note that the present embodiment provided herein adjusts both the areas and the confidence values More particularly, the hypothesis evaluator I228 may perform any or most of the following tasks: (30,!) it utilizes environmental information to improve and reconcile location hypotheses supplied by the first order models I224. A basic premise in this context is that the accuracy of the individual first order models may be affected by various environmental factors such as. for example, the season of the year, the tiriie of day, the weather conditions, the presence of buildings, base station failures, etc; CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 (30.2) it enhances the accuracy of an initial location hypothesis generated by an l0M by using the initial location hypothesis as. essentially, a query or index into the location signature data base I320 for obtaining a corresponding enhanced location hypothesis, wherein the enhanced location hypothesis has both an adjusted target MS location area estimate and an adjusted confidence based on past performance of the FOM in the location service surrounding the target MS location 5 estimate of the initial location hypothesis; (30.3) it determines how well the associated signal characteristics used for locating a target MS compare with particular verified loc sigs stored in the location signature data base I320 (see the location signature data base section for further discussion regarding this aspect of the invention). That is, fora given location hypothesis, verified loc sigs (which were previously obtained from one or more verified locations of one or more l‘|S's) are retrieved for an area corresponding to the location area estimate of the location hypothesis, and the to signal characteristics of these verified loc sigs are compared with the signal characteristics used to generate the location hypothesis for determining their similarities and subsequently an adiustmem to the confidence of the location hypothesis (and/or the size of the location area estimate); (30.4) the hypothesis evaluator I228 determines if (or how well) such location hypotheses are consistent with well known physical constraints such as the laws of physics for example. if the difference between a previous (most likely) location estimate of a target HS and a 15 location estimate by a cunent location hypothesis requires the MS to: (al) move at an unreasonably high rate of speed (e.g.. 200 mph), or (bl) move at an unreasonably high rate of speed for an area (eg., 80 mph in a corn patch), or (cl) make unreasonably sharp velocity changes (e.g., from 60 mph in one direction to 60 mph in the opposite direction in 4 sec). then the confidence in the current location Hypothesis is likely to be 20 reduced. Alternatively, if for example, the difference between a previous location estimate of a target MS and a current location hypothesis indicates that the MS is: (a2) moving at an appmpriate velocity for the area being traversed. or (b2) moving along an established path (e.g., a freevray), 25 then the confidence in the current location hypothesis may be increased. (30.5) the hypothesis evaluator I228 determines consistencies and inconsistencies between location hypotheses obtained from different first order models. for example, if two such location hypotheses, for substantially the same timestamp, have estimated location areas where the target MS is lilcely to be and these areas substantially overlap, then the confidence in both such location hypotheses may be increased. Additionally, note that a velocity of an MS may be determined (via deltas of successive location hypotheses from one or 30 more first order models) even when there is low confidence in the location estimates for the MS, since such deltas may, in some cases. be more reliable than the actual target MS location estimates; (30.6) the hypothesis evaluator I228 determines new (more accurate) location hypotheses from other location hypotheses. Forexample. this module may generate new hypotheses from currently active ones by decomposing a location hypothesis having a target MS 56 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/ 15892 location estimate intersecting two radically different area types. Additionally, this module may generate location hypotheses indicating areas of poor reception; and (30.7) the hypothesis evaluator I228 detennines and outputs a most likely location hypothesis for a target MS. Note that the hypothesis evaluator may accomplish the above tasks. (30.l) - (30.7), by employing various data processing tools including, but not limited to, fuzzy mathematics, genetic algorithms, neural networks, expert systems and/or blackboard systems. Note that, as can be seen in Figs 6 and 7, the hypothesis evaluator I228 includes the following four high level modules for processing output location hypotheses from the first order models I224: a context adjuster I326, a hypothesis analyzer I332, an MS status repository I338 and a most likelihood estimator I334. These four modules are briefly described hereinbelow. Context Adjuster Introduction. The context adjuster I326 nrodule enhances both the comparability and predictability of the location hypotheses output by the first order models I224. In particular, this module modifies location hypotheses received from the f0l‘ls I224 so that the resulting location hypotheses output by the context adjuster I326 may be further processed unifonnly and substantially without concern as to differences in accuracy between the first order models from which location hypotheses originate. In providing this capability, the context adjuster I326 may adjust or modify various fields of the input location hypotheses. In particular, fields giving target MS I40 location estimates and/orconfrdence values forsuch estimates may be modified by the comext adjuster I326. Further. this module may determine those factors that are perceived to impact the perceived accuracy (e.g., confidence) of the location hypotheses: (a) differently between FOI’|s, and/or (b) with substantial effect. For instance, environmental tliaracteristics may be taken into account here, such as time of day, season, month, weather, geographical area tategorizations (eg, dense urban, urban, suburban, niral, mountain, etc), area subcategorizations (e.g., Iieavily treed, hilly, high traffic area, etc). A detailed description of one embodiment of this module is provided in APPENDIX D hereinbelow. Note that, the embodiment described herein is simplified for illustration purposes such that only the geographical area categorizations are utilited in adjusting (i.e., modifying) location hypotheses. But, it is an important aspect of the present invention that various categorizations, sudi as those memioned immediately above, may be used for adjusting the location hypotheses. That is, categories such as, for example: (2) urban, hilly, high traffic at 5pm, or (b) ruraL flat, heavy tree foliage density in summer may be utilized as one skilled in the art will understand from the descriptions contained hereinbelow. Accordingly, the present invention is not limited to the factors explicitly mentioned here. That is, it is an aspect of the present invention to be extensible so that otherenviionniental factors of the coverage area I20 affecting the accuracy of location hypotheses may also be incorporated into the comext adjuster I326. It is also an imponant and novel aspect of the context adjuster I326 that the methods for adjusting location hypotheses provided in this module may be generalized and thereby also utilized with multiple hypothesis computational architectures related to various applications wherein a terrain, surface, volume or other “geometric” interpretation (eg, a metric space of statistical samples) may be placed on a large body of stored application data for relating hypothesized data to verified data. Moreover. it is important to note that various techniques for “visualizing data” may provide such a geometric interpretation. Thus, the methods herein may be utilized in applications such as: S7 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15,892 (a) sonar, radar, x-ray or infrared identification of objects such as occurs in robotic navigation, medical image analysis, geological, and radar imaging. More generally, the novel computational paradigm of the context adjuster I326 may be utilized in a number of applications wherein there is a large body of archived infomtation providing verified or actual application process data related to the past pertonnance of the application process. It is worth mentioning that the computational paradigm used in the context adjuster I326 is a hybrid of a hypothesis adjuster and a data base query mechanism. for example, the context adjuster l326 uses an input (location) hypothesis both as an hypothesis and as a data base query or index into the location signature data base l320 for constnrcting a related but ntore accurate location hypothesis. Accordingly, substantial advantages are provided by this hybrid architecture. such as the following two advantages. As a first advantage, the context adjuster I326 reduces the fikelihood that a feedback mechanism is necessary to the initial hypothesis generators (i.e., f0l1s I224) for periodically adjusting default evaluations of the goodness or confidence in the hypotheses generated. That is. since each hypothesis generated is, in effect. an index imo a data base or archive of verified application (age location) data, the context adjuster B26, in turn, generates newcorresponding hypotheses based on the actual or verified data retrieved from an archival data base. Thus, as a result, this architecture tends to separate the computations of the initial hypothesis generators (eg, the F0lls I224 in the presem MS location application) fmm any further processing and thereby provide a more modular, maintainable and flexible computational system. As a second advantage, the context adjuster I326 tends to create hypotheses that are more accurate than the hypotheses generated by the initial hypotheses generators. That is, for each hypothesis, H, provided by one of the initial hypothesis generators, G (e.gw a FOM I224), a conesponding enhanced hypothesis, provided by the context adjuster B26, is generated by mapping the past performance of G into the archived verilied application data (as will be discussed in detail hereinbelow). in particular, the context adjuster hypothesis generation is based on the archived verified (or known) performance application data that is related to both G and H. For example, in the present wireless location application, if a TOM I224, G, substantially consistently generates, in a particular geographical area, location hypotheses that are biased approximately I000 feet north of the actualverifred MS MO location, then the context adjuster I326 can generate corresponding hypotheses without this bias. Thus. the context adjuster I326 tends to filter out inaccuracies in the initially generated hypotheses Therefore in a multiple hypothesis architecture where typically the generated hypotheses may be evaluated and/or combined for providing a “most likely" result, it is believed that a plurality of relatively simple (and possibly inexact) initial hypothesis generators may be used in conjunction with the hybrid computational paradigm represented by the context adjuster I326 for providing enhanced hypotheses with substantially greater accuracy. Add itionally, note that this hybrid paradigm applies to otherdomains that are not geogmphically based. For instance, this hybrid paradigm applies to many prediction and/or diagnostic applications for whiclr (a) the application data and the application are dependent on a number of parameters whose values characterize the range of outputs for the application. That is. there is a set of parameters, p,, p,, p3, ., , p,, fmm which a parameter space p, x p, x pjx x p,, is derived whose points characterize the actual and estimated (or predicted) outcomes. As examples, in the MS location system, p, = latitude and p, = longitude; 10 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/15892 (b) there is historical data fmm which points forthe parameter space. p. x pzx p,x ...x p,, can be obtained. wherein this data relates to (or indicates) the performance of the application. and the points obtained from this data are relatively dense in the space (at least around the likely future actual outcomes that the application is expected to predict or diagnose). For example, such historical data may associate the predicted outcomes of the application with corresponding actual outcomes; (c) there is a metric ordistance-like evaluation function that can be applied to the pammeter space for indicating relative closeness or accuracy of points in the parameter space, wherein the evaluation function provides a measurement of closeness that is related to the actual perfomtance of the application. Note that there are numerous applications for which the above criteria are applicable. For instance. computer aided control of chemical processing plants are likely to satisfy the above criteria. Certain robotic applications may also satisfy this criteria. In fact, it is believed that a wide range of signal processing applications satisfy this criteria. MS Status Repository introduction The MS status repository I338 is a run-time storage manager for storing location hypotheses from previous activations of the location engine l39 (as well as for storing the output “most likely" target MS location estimate(s)) so that a target MS I40 may be tracked using target MS location hypotheses from previous location engine I39 activations to determine, for example, a movement of the target MS MO between evaluations of the target HS location. Location Hypothesis Analyzer Introduction. The location hypothesis analyzer I332, adjusts confidence values of the location hypotheses, according to: (a) heuristics and/or statistical methods related to how well the signal characteristics for the generated target MS location hypothesis matches with previously obtained signal characteristics forverilied MS locations (b) heuristics related to how consistent the location hypothesis is with physical laws, and/or highly probable reasonableness conditions relating to the location of the target MS and its movement characteristics for example, such heuristics may utilize knowledge of the geographical terrain in which the MS is estimated to be, and/or. for instance, the MS velocity, acceleration or extrapolation of an MS position. velocity, oracceleration (c) generation of additional location hypotheses whose MS locations are consistent with, lor example, previous estimated locations for the target MS. As shown in Figs. 6 and 7,the hypothesis analyzer I332 module receives (potentially) modified location hypotheses from the context adjuster I326 and performs additional location hypothesis processing that is likely to be common and generic in analyzing most location hypotheses. More specilimlly, the hypothesis analyzer l332 may adjust either or both of the target MS I40 estimated location and/or the confidence of a location hypothesis. ln brief. the hypothesis analyzer l332 receives target MS I40 location hypotheses fmm the context analyzer I336. and depending on the time stamps of newly received location hypotheses and any previous (ie.. older) target MS location hypotheses that may still be currently available to the hypothesis analyzer l332. the hypothesis analyzer may: (a) update some of the older hypotheses by an extrapolation module, 59 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/U S97! 15892 (b) utilize some of the old hypotheses as previous target MS estirrrates lor use in tracking the target MS MO, and/or (c) if sufficiently old, then delete the older location hypotheses. Note that both the newly received location hypotheses and the previous location hypotheses that are updated (i.e., extrapolated) and still remain in the hypothesis analyzer I332 will be denoted as “cunent location hypotheses" or “currently active location hypotheses”. The modules within the location hypothesis analyzer I332 use various types of application specific knowledge likely substantially independent from the computations by the folls I224 when providing the corresponding original location hypotheses. That is, since it is aspect of at leastone embodiment of the present invention that the f0Ms I224 be relatively straightforward so that they may be easily to modified as well as added or deleted, the processing, lor example. in the hypothesis analyzer I332 (as with the context adjuster I326) is intended to compensate, when necessary, lorthis straightlorwardness by providing substantially generic MS location processing capabilities that can require a greater breadth of application understanding related to wireless signal characteristics ol the coverage area I20. Accordingly, the hypothesis analyzer I332 may apply various heuristics that, for example. change the confidence in a location hypothesis depending on how well the location hypothesis (and/or a series of location hypotheses lmm e.g. the same f0l’l I224): (a) conlorms with the laws of physics, (b) conforms with known characteristics of location signature clusters in an area of the location hypothesis MS MO estimate, and (c) conforms with highly likely heuristic constraint knowledge. in particular, as illustrated best in fig. 7, the location hypothesis analyzer l332 may utilize at least one of a blackboard system and/or an expert system lor applying various application specific heuristics to the location hypotheses output by the context adjuster I326. More precisely, the location hypothesis analyzer B32 includes. in one embodiment. a blackboard manager for managing processes and data of a blackboard system. Additionally, note that in a second embodiment, where an expert system is utilized instead of a blackboard system, the location hypothesis analyzer provides an expert system inlerence engine for the expert system. Note that additional detail on these aspects of the invention are provided hereinbelow. Additionally, note that the hypothesis analyzer I332 may activate one or more extrapolation procedures to extrapolate target MS MO location hypotheses already processed. Thus, when one or more new location hypotheses are supplied (by the context adjuster l224) having a substantially more recent timestamp, the hypothesis analyzer may invoke an extrapolation module (r.e., location extrapolator I432, Fig.7) for adjusting any previous location hypotheses lor the same target MS I40 that are still being used by the location hypothesis analyzer so that all target MS location hypotheses (for the same target MS) being concurrently analyzed are presumed to be lor substantially the same time. Accordingly, such a previous location hypothesis that is. lor example, I5 seconds older than a newly supplied location hypothesis (lrom perhaps a different TOM I224) may have both: (a) an MS location estimate changed (eg, to account for a movement of the target MS), and (b) its confidence changed (e.g., to rellect a reduced confidence in the accuracy ol the location hypothesis). it is important to note that the architecture of the present invention is such that the hypothesis analyzer I332 has an extensible architecture. That is, additional location hypothesis analysis modules may be easily integrated into the hypothesis analyzer l332 as further understanding regarding the behaviorol wireless signals within the service area I20 becomes available. Conversely, sonre analysis modules may not be required in areas having relatively predictable signal patterns. Thus, in such service areas, such unnecessary modules maybe easily removed or not even developed.. Most Likelihood Estimator Introduction 20 25 CA 02265875 1999-03-09 wo 93/10307 PCT /U S97/ 15892 The most liloelihood estimator I344 is a module for determining a “most likely" location estimate fora target MS being located by the location engine I39. The most likelihood estimator I344 receives a collection of active or relevant location hypotheses lmm the hypothesis analyzer I332 and uses these location hypotheses to determine one or nrore most likely estimates lorthe target MS I40. Still referring to the hypothesis evaluator I228, it is important to note that not all the above memioned modules are required in all embodiments of the present invention. In particular, for some coverage areas I20, the hypothesis analyzer I332 may be unnecessary. Accordingly, in such an embodiment, the enhanced location hypothesis output by the context adjuster I326 are provided directly to the most likelihood estimator I344. Control and Output Gating Modules A fourth functional group of location engine I39 modules is the control and output gating modules which includes the location center control subsystem I350, and the output gateway I356. The location control subsystem I350 provides the highest level ol control and monitoring of the data processing performed by the location center I42. In particular, this subsystem perfomts the following functions: (a) controls and monitors location estimating processing for each target MS I40. Note that this includes high level exception or enor handling functions; (b) receives and routes external inlonnation as necessary. For instance. this subsystem may receive (via, eg. the public telephone switching network and Intemet I362) such environmental infonnation as increased signal noise in a particular service are due to increase traffic, a change in weatherconditions, a base station I22 (or other infrastnrcture provisioning), change in operation status (e.g, operational to inactive); (c) receives and directs location processing requests from other location centers I42 (via, e.g, the Internet); (d) performs accounting and billing procedures; (e) interacts with location center operators by. for example, receiving operator commands and pmviding output indicative of processing resources being utilized and malfunctions: (I) providesaccess to output requirements forvarious applications requesting location estimates. iorexample,an Internet location request from a truclcing company in Los llngeles to a location center I42 in Denver may only want to know if a particular truck or driver is within the llenverarea. Alternatively. a local medical rescue unit is likely to request a precise a location estimate as possible. Note that in fig. 6 (a) - (d) above are, at least at a high level, perlonned by utilizing the operator interface I374 . Relerring now to the output gateway I356, this module routes target I18 I40 location estimates to the appropriate location application(s). For instance, upon receiving a location estimate from the most likelihood estimator I344, the output gateway I356 may determine that the location estimate is loran automobile being tracked by the police and therefore must be provided must be provided according to the particular pmtocol 6| 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 System Tuning and Adaptation: The Adaptation Engine A fifth functional group of location engine I39 modules provides the ability to enhance the MS locating reliability and/or accuracy of the present invention by providing it with the capability to adapt to particular operating configurations, operating conditions and wireless signaling environments without performing intensive manual analysis of the perlomrance of various embodiments of the location engine l39. That is, this functional group automatically enhances the perfonnance of the location engine for locating MSs I40 within a particular coverage area I20 using at least one wireless network infrastructure therein. More precisely, this functional group allows the present invention to adapt by tuning or optimizing certain system parameters according to location engine I39 location estimate accuracy and reliability. There are a number location engine I39 system parameters whose values affect location estimation, and it is an aspect of the present invention that the MS location processing performed should become increasingly better at locating a target MS I40 not only through building an increasingly more detailed model of the signal characteristics of location in the coverage area I20 such as discussed above regarding the location signature data base I320, but also by providing automated capabilities for the location center processing to adapt by adjusting or “tuning” the values of such location center system parameters. Accordingly, the present invention includes a module, denoted herein as an “adaptation engine" I382, that perlonns an optimitation procedure on the location center I42 system parameters either periodically or concurrently with the operation of the location center in estimating MS locations. That is, the adaptation engine I382 directs the modifications of the system parameters so that the location engine I39 increases in overall accuracy in locating target MSs I40. In one embodiment, the adaptation engine I382 includes an embodiment of a genetic algorithm as the mechanism for modifying the system parameters Genetic algorithms are basically search algorithms based on the mechanics of natural genetics The genetic algorithm uti|i1ed herein is included in the form of pseudo code in APPENDIX B. Note that to apply this genetic algorithm in the context of the location engine I39 architecture only a "coding scheme" and a "fitness function" are required as one skilled in the art will appreciate. Moreover, it is also within the scope of the present invention to use modified or different adaptive and/or tuning mechanisms. for further information regarding such adaptive mechanisms, the following references are incorporated herein by reference: Goldberg, D. E. (I 989). Genetic algorithms for search, optimization, and machine learning. Reading, MA: Addison-Wesley Publishing Company; and Holland. J. H. (I975) Adaptation in natural and artificial systems. Ann Arbor, MI: The University of Michigan Press. Implementations of first Order Models further descriptions of various first order models l224 are provided in this section. Distance first Order Models (TOA/T DOA) As discussed in the Location Center Architecture Overview section herein above, distance models determine a presumed direction and/or distance that a target MS I40 is from one or more base stations I22. In some embodiments of distance models. the target MS location estimate(s) generated are obtained using radio signal analysis techniques that are quite general and therefore are not capable of taking into account the peculiarities of the topography of a particular radio coverage area. For example, substantially 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 all radio signal analysis techniques using conventional procedures (or formulas) are based on “signal characteristic measurements" such as: (a) signal timing measurements (e.g., TOA and TDOA), (b) signal strength measurements. and/or (c) signal angle of arrival measurements. Furthermore, such signal analysis techniques are likely predicated on certain very general assumptions that can not fully account for signal attenuation and multipath clue to a particular radio coverage area topography. faking CDl‘lll or TDMA base station network as an example. each base station (BS) I22 is required to emit a constant signal-strength pilot channel pseudo-noise (PN) sequence on the forward link channel identified uniquely in the network by a pilot sequence offset and frequency assignment. It is possible to use the pilot channels of the active. candidate, neighboring and remaining sets, maintained in the target MS, for obtaining signal characteristic measurements (e.g., TOA and/or TDOA measurements) between the target MS l40 and the base stations in one or more of these sets. Based on such signal characteristic measurements and the speed of signal propagation, signal characteristic ranges or range differences related to the location of the target MS I40 can be calculated. Using IOA and/or IDOA ranges as exemplary, these ranges can then be input to either the radius-radius multilateration or the time difference multilateration algorithms along with the known positions of the corresponding base stations I22 to thereby obtain one or more location estimates of the target MS M0. for example, if there are, four base stations I22 in the active set, the target MS I40 may cooperate with each of the base stations in this set to provide signal arrival time measurements. Accordingly, each of the resulting four sets of three of these base stations I22 may be used to provide an estimate of the target MS MO as one skilled in the art will understand. Thus, potentially (assuming the measurements for each set of three base stations yields a feasible location solution) there are four estimates for the location of the target MS I40. Further. since such measurements and BS l22 positions can be sent either to the network or the target MS MO, location can be determined in either entity. Since many of the signal measurements utilized by embodiments of distance models are subject to signal attenuation and multipath due to a particular area topography. Many of the sets of base stations from which target MS location estimates are desired may result in either no location estimate. or an inaccurate location estimate. Accordingly, some embodiments of distance FOMs may attempt to mitigate such ambiguity or inaccuracies by, e.g., identifying discrepancies (or consistencies) between arrival time measurements and other measurements (e.g., signal strength), these discrepancies (or consistencies) may be used to filter out at least those signal measurements and/or generated location estimates that appear less accurate. In particular, such identifying may filtering can be performed by, for example, an expert system residing in the distance FOM. A second approach for mitigating such ambiguity or conflicting MS location estimates is particularly novel in that each of the target MS location estimates is used to generate a location hypothesis regardless of its apparent accuracy. Accordingly, these location hypotheses are input to an alternative embodiment of the context adjuster I326 that is substantially (but not identical to) the context adjuster as described in detail in APPENDIX D so that each location hypothesis may be adjusted to enhance its accuracy. 63 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 In contradistinction to the embodiment of the context adjuster I326 of APPENDIX D, where each location hypothesis is adjusted according to past performance of its generating TOM I224 in an area of the initial location estimate of the location hypothesis (the area. e.g., determined as a function of distance from this initial location estimate), this alternative embodiment adjusts each of the location hypotheses generated by a distance first order model according to a past performance of the model as applied to signal characteristic measurements from the same set of base stations I22 as were used in generating the location hypothesis. That is, instead of only using only an identification of the distance model (i.e., its FOIl_II)) to, for example, retrieve archived location estimates generated by the model in an area of the location hypothesis’ estimate (when determining the model's past performance), the retrieval retrieves only the archived location estimates that are, in addition, derived from the signal characteristics measurement obtained from the same collection of base stations I22 as was used in generating the location hypothesis. Thus, the adjustment performed by this embodiment of the context adjuster I326 adjusts according to the past performance of the distance model and the collection of base stations I22 used. Coverage Area first Order Model lladio coverage area of individual base stations I22 may be used to generate location estimates of the target MS I40. Although a first order model I224 based on this notion may be less accurate than other techniques, il a reasonably accurate llf coverage area is known for each (or most) of the base stations I22, then such a TON (denoted hereinafter as a "coverage area first order model" or simply “coverage area model") may be very reliable. To determine approximate maximum radio frequency (RF) location coverage areas, with respect to BSs I22, antennas and/or sector coverage areas, for a given class (or classes) of (e.g., CDMA or TDMA) mobile station(s) I40, location coverage should be based on an IlS‘s ability to adequately detect the pilot channel, as opposed to adequate signal quality Ior purposes of carrying user-acceptable traffic in the voice channel. Note that more energy is necessary for traffic channel activity (typically on the order of at least -94 to ~| 04 dBm received signal strength) to support voice, than energy needed to simply detect a pilot channel's presence for location purposes (typically a maximum weakest signal strength range of between -I04 to -I I0 dllm), thus the “Location Coverage Area" will generally be a larger area than that of a typical "Voice Coverage Area”, although industry studies have found some occurrences of “no-coverage" areas within a larger covered area. An example of a coverage area including both a “dead zone". i.e.. area of no coverage, and a "notch" (of also no coverage) is shown in Fig. IS. The approximate maximum Ill coverage area for a given sector of (more generally angular range about) a base station I22 may be represented as a set of points representing a polygonal area (potentially with, e.g., holes therein to account for dead zones and/or notches). Note that if such polygonal RF coverage area representations can be reliably determined and maintained over time (for one or more BS signal power level settings). then such representations can be used in providing a set theoretic or Venn diagram approach to estimating the location of a target MS I40. Coverage area first order models utilize such an approach. 25 30 CA 02265875 1999-03-09 wo 98/10307 PCT/US97/15892 One embodiment, a coverage area model utilizes both the detection and non-detection of base stations l22 by the target MS I40 (conversely, of the MS by one or more base stations l22) to define an area where the target MS I40 may likely be. A relatively straightforward application of this technique is to: (a) find all areas of intersection for base station llf coverage area representations, wherein: (i) the corresponding base stations are on-line for communicating with MS: MO; (ii) the llf coverage area representations are deemed reliable for the power levels of the on-line base stations; (iii) the on-line base stations having reliable coverage area representations can be detected by the target MS; and (iv) each intersection must include a predetermined number of the reliable RF coverage area representations (e.g., 2 or 3); and (b) obtain new location estimates by subtracting lrom each of the areas of intersection any of the reliable Rf coverage area representations for base stations I22 that can not be detected by the target MS. Accordingly, the new areas may be used to generate location hypotheses. Location Base Station first Order Model In the location base station (LBS) model (FOM I224), a database is accessed which contains electrical, radio propagation and coverage area characteristics of each of the location base stations in the radio coverage area. The LBS model is an active model, in that it can probe or excite one or more particular LBSs I52 in an area for which the target MS I40 to be located is suspected to be placed. Accordingly, the LES model may receive as input a most likely target MS I40 location estimate previously output by the location engine B9 of the present invention, and use this location estimate to determine which (if any) l.BSs I52 to activate and/or deactivate for enhancing a subsequent location estimate of the target MS. Moreover, the feedback from the activated LBSs l52 may be provided to other FOMs l224. as appropriate, as well as to the LBS model. However, it is an important aspect of the LBS model that when it receives such feedback, it may output location hypotheses having relatively small target MS MO location area estimates about the active LBS: I52 and each such location hypothesis also has a high confidence value indicative of the target MS MO positively being in the corresponding location area estimate (e.g., a confidence value of .9 to + l), or having a high confidence value indicative of the target MS I40 not being in the corresponding location area estimate (i.e., a confidence value of -0.9 to -I). Note that in some embodiments of the LBS model, these embodiments may have functionality similar to that of the coverage area first order model described above. Further note that for LBSs within a neighborhood of the target MS wherein there is a reasonable chance that with movement of the target MS may be detected by these LBSs. such LBS: may be requested to periodically activate. (Note, that it is not assumed that such lBSs have an on-line external power source; eg, some may be solar powered). Moreover, in the case where an LBS I52 includes sufficient electronics to carry voice communication with the target MS I40 and is the primary BS for the target MS (or alternatively. in the active or candidate set), then the LBS model will not deactivate this particular LBS during its procedure of activating and deactivating various LBS: l52. 20 25 30 CA 02265875 1999-03-09 WO 98/10307 Stochastic First Order Model PCT/US97/l 5892 The stochastic first order models may use statistical prediction techniques such as principle decomposition, partl least squares, partial least squares, or other regression techniques for predicting, for example, expected minimum and maximum distances of the target MS lrom one or more base stations I22, e.g., Bollenger Bands. Additionally, some embodiments may use Markov processes and Random Walks (predicted incremental MS movement) for determining an expected area within which the target MS I40 is likely to be. That is, such a process measures the incremental time dillerences of each pilot as the MS moves for predicting a size of a location area estimate using past MS estimates such as the verified location signatures in the location signature data base I320. Pattern Recognition and Adaptive first Order Models It is a particularly important aspect of the present invention to provide: (a) one or more FOMs I224 that generate target MS l40 location estimates by using pattern recognition or associativity techniques, and/or (b) one or more FOMs I224 that are adaptive or trainable so that such FOMs may generate increasingly more accurate target MS location estimates from additional training. Statistically Based Pattern Recognition first Order Models Regarding TOMs 1224 using pattern recognition or associativity techniques, there are many such techniques available. For example, there are statistically based systems such as “CAllT" (anacronym lor Classification and Regression Trees) by ANGOSS Software International limited of Toronto, Canada that may be used for automatically for detecting or recognizing patterns in data that were unprovided (and likely previously unknown). Accordingly, by imposing a relatively line mesh or grid of cells of the radio coverage area, wherein each cell is entirely within a particular area type categorization such as the transmission area types (discussed in the section, “Coverage Area: Area Types And Their Determination" above), the verified location signature clusters within the cells of each area type may be analyzed for signal characteristic patterns. If such patterns are found, then they can be used to identily at least a likely area type in which a target MS is likely to be located. That is, one or more location hypotheses may be generated having target MS l40 location estimates that cover an area having the likely area type wherein the target MS I40 is located. Further note that such statistically based pattern recognition systems as “CAllT" include soltware code generators for generating expert system soltware embodiments for recognizing the patterns detected within a training set (e.g., the verified location signature clusters). Accordingly, although an embodiment of a FOM as described here may not be exceedingly accurate, it may be very reliable. Thus, since a lundamental aspect of the present invention is to use a plurality MS location techniques lor generating location estimates and to analyze the generated estimates (likely after being adjusted) to detect patterns of convergence or clustering among the estimates. even large MS location area estimates are useful. for example, it can be the case that four dillerent and relatively large MS location estimates, each having very high reliability, have an area of intersection that is acceptably precise and inherits the very high reliability from each of the large MS location estimates from which the intersection area was derived. 66 10 20 25 CA 02265875 1999-03-09 W0 98/10307 PCT/U897/15392 A similar statistically based FOM I224 to the one above may be provided wherein the radio coverage area is decomposed substantially as above, but addition to using the signal characteristics for detecting useful signal patterns, the specific identifications of the base station I22 providing the signal characteristics may also be used. Thus. assuming there is a sufficient density of verified location signature clusters in some of the mesh cells so that the statistical pattern recognizer can detect patterns in the signal characteristic measurements, an expert system may be generated that outputs a target MS MO location estimate that may provide both a reliable and accurate location estimate of a target MS I40. Adaptive/Trainable first Order Models Adaptive/irainable first Order Models The term adaptive is used to describe a data processing component that can modify its data processing behavior in response to certain inputs that are used to change how subsequent inputs are processed by the component. Accordingly. a data processing component may be “explicitly adaptive" by modifying its behavior according to the input of explicit instructions or control data that is input for changing the component's subsequent behavior in ways that are predictable and expected. That is, the input encodes explicit instructions that are known by a user of the component. Alternatively, a data processing component may be “implicitly adaptive" in that its behavior is modified by other than instructions or control data whose meaning is known by a user of the component. For example, such implicitly adaptive data processors may learn by training on examples, by substantially unguided exploration of a solution space, or other data driven adaptive strategies such as statistically generated decision trees. Accordingly, it is an aspect of the present invention to utilize not only explicitly adaptive MS location estimators within FOMs I224, but also implicitly adaptive MS location estimators. In particular, artificial neural networks (also denoted neural nets and ANNs herein) are used in some embodiments as implicitly adaptive MS location estimators within FOMs. Thus, in the sections below, neural net architectures and their application to locating an MS is described. Artificial Neural Networks For MS Location Artificial neural networks may be particularly useful in developing one or more first order models I224 for locating an MS I40, since, for example, ANN: can be trained for classifying and/or associatively pattern matching of various RF signal measurements such as the location signatures. That is, by training one or more artificial neural nets using RF signal measurements from verified locations so that RF signal transmissions characteristics indicative of particular locations are associated with their corresponding locations, such trained artificial neural nets can be used to provide additional target MS I40 location hypotheses. Moreover, it is an aspect of the present invention that the training of such artificial neural net based f0Ms (ANN f0Ms) is provided without manual intervention as will be discussed hereinbelow. 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 Artificial Neural Networks That Converge on Near Optimal Solutions It is as an aspect of the present invention to use an adaptive neural network architecture which has the ability to explore the parameter or matrix weight space corresponding to a ANN for determining new configurations of weights that reduce an objective or error function indicating the error in the output of the ANN over some aggregate set of input data ensembles. Accordingly, in one embodiment, a genetic algorithm is used to provide such an adaptation capability. However, it is also within the scope of the present invention to use other adaptive techniques such as, for example, simulated annealing. cascade correlation with multistarts, gradient descent with multistarts, and truncated Newton’: method with multistarts,as one skilled in the art of neural network computing will understand. Artificial Neural Networks as MS location Estimators for first Order Models Although there have been substantial advances in artificial neural net computing in both hardware and software, it can be difficult to choose a particular ANN architecture and appropriate training data for yielding high quality results. In choosing a ANN architecture at least the following three criteria are chosen (either implicitly or explicitly): (a) a learning paradigm: i.e., does the ANN require supervised training (i.e., being provided with indications of correct and incorrect performance), unsupervised training, or a hybrid of both (sometimes referred to as reinforcement); (b) a collection of learning rules for indicating how to update the ANN; (c) a learning algorithm for using the learning rules for adjusting the ANN weights. furthermore, there are other implementation issues such as: (cl) how many layers a artificial neural net should have to effectively capture the patterns embedded within the training data. For example. the benefits of using small ANN are many. less costly to implement, faster, and tend to generalize better because they avoid overfitting weights to training patterns. That is, in general, more unknown parameters (weights) induce more local and global minima in the error surface or space. However, the error surface of smaller nets can be very rugged and have lew good solutions, making it difficult for a local minimization algorithm to find a good solution from a random starting point as one skilled in the art will understand; (e) how many units or neurons to provide per layer; (f) how large should the training set be presented to provide effective generalization to non-training data (g) what type of transfer functions should be used. However, the architecture of the present invention allows substantial flexibility in the implementation of ANN for F0l‘|s I224. In particular, there is no need to choose only one artificial neural net architecture and/or implementation in that a plurality of ANNs may be accommodated by the architecture of the location engine I39. furthermore, it is important to keep in mind that it may not be necessary to train a ANN for a FOM as rigorously as is done in typical ANN applications since the accuracy and reliability in estimating the location of a target MS MD with the present invention comes from synergistically utilizing a plurality of different l‘lS location estimators, each of which may be undesirable in terms ol accuracy and/or reliability in some areas, but when their estimates 68 10 15 20 25 30 CA 02265875 1999-03-09 W0 98,1030-; PCT‘/US97/15892 are synergistically used as in the location engine l39, accurate and reliable location estimates can be attained. Accordingly, one embodiment of the present invention may have a plurality of moderately well trained ANNs having different neural net architectures such as: multilayer perceptrons, adaptive resonance theory models, and radial basis function networks. Additionally, many of the above mentioned ANN architecture and implementation decisions can be addressed substantially automatically by various commercial artificial neural net development systems such as: “N EUROGENETIE OPTIMIZER" by Biofomp Systems, wherein genetic algorithms are used to optimize and configure ANNs, and artificial neural network hardware and software products by Accurate Automation Corporation of Chattanooga, Tennessee, such as “ACCURATE AUTOMATION NEURAL N ETWORK TOOLS. Artificial Neural Network Input and Output It is worthwhile to discuss the data representations for the inputs and outputs of a ANN used for generating MS location estimates. llegarding ANN input representations, recall that the signal processing subsystem I220 may provide various Rf signal measurements as input to an ANN (such as the RF signal measurements derived from verified location signatures in the location signature data base i320). lorexample, a representation of a histogram of the frequency of occurrence of CDMA fingers in a time delay vs. signal strength 2-dimensional domain may be provided as input to such an ANN. In particular. a 2-dimensional grid of signal strength versus time delay bins may be provided so that received signal measurements are slotted into an appropriate bin of the grid. In one embodiment, such a grid is a six by six array of bins such as illustrated in the left portion of Fig. I4. That is, each of the signal strength and time delay axises are partitioned into six ranges so that both the signal strength and the time delay of RF signal measurements can be slotted into an appropriate range, thus determining the bin. Note that lil signal measurement data (i.e., location signatures) slotted into a grid of bins provides a convenient mechanism for classifying lll measurements received over time so that when each new Rf measurement data is assigned to its bin, a counter for the bin can be incremented. Thus in one embodiment, the RF measurements for each bin can be represented pictorially as a histogram. In any case, once the lil measurements have been slotted into a grid, various filters may be applied for filtering outliers and noise prior to inputting bin values to an ANN. Further, various amounts of data from such a grid may be provided to an ANN. In one embodiment, the tally from each bin is provided to an ANN. Thus, as many as I08 values could be input to the ANN (two values defining each bin, and a tally lor the bin). However, other representations are also possible. For instance, by ordering the bin tallies linearly, only 36 need be provided as ANN input. Alternatively, only representations of bins having the highest tallies may be provided as ANN input. Thus, lorexample, if the highest IO bins and their tallies were provided as ANN input, then only 20 inputs need be provided (i.e., l0 input pairs, each having a single bin identifier and a corresponding tally). in addition, note that the signal processing subsystem I220 may also obtain the identifications of other base stations I22 (I52) for which their pilot channels can be detected by the target MS I40 (i.e., the forward path), or for which the base stations can detect a signal from the target MS (i.e., the reverse path). Thus. in order to effectively utilize substantially all pertinent location RF signal measurements (i.e, from location signature data derived from communications between the target MS I40 and the base station 69 I0 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 infrastructure), a technique is provided wherein “a plurality of ANNs may be activated using various portions ol an ensemble of location signature data obtained. However, before describing this technique, it is worthwhile to note that a naive strategy of providing input to a single ANN for locating target MS: throughout an area having a large number of base stations (e.g., 300) is likely to be undesirable. That is, given that each base station (antenna sector) nearby the target MS is potentially able to provide the ANN with location signature data, the ANll would have to be extremely large and therefore may require inordinate training and retraining. for example. since there may be approximately 30 to 60 ANN inputs per location signature, an ANN for an area having even twenty base stations I22 can require at least 600 input neurons. and potentially as many as L420 (i.e., 20 base stations with 70 inputs per base station and one input for every one of possibly 20 additional surrounding base stations in the radio coverage area I20 that might be able to detect. or be detected by, a target MS I40 in the area corresponding to the ANN). Accordingly, the technique described herein limits the number of input neurons in each ANN constructed and generates a larger number ol these smaller All Ns. That is, each ANN is trained on location signature data (or, more precisely, portions of location fiummdmmfimmummmwmmmuflmmmmmMuuwWwmmMxMwmmmmmmwummmmmflmm enhen (Al) location signature data (e.g., signal strength/time delay bin tallies) corresponding to transmissions between an MS I40 and a relatively small number of base stations l22 in the area Am, For instance, location signature data obtained from, for example, four base stations l22 (or antenna sectors) in the area AM, Note, each location signature data cluster includes fields describing the wireless communication devices used; e.g., (i) the make and model of the target MS; (ii) the current and maximum transmission power; (iii) the MS battery power (instantaneous or current); (iv) the base station (sector) current power level; (v) the base station make and model and revision level; (vi) the air interface type and revision level (of, e.g., CDMA, TDMAorAMP9. MnamummmmnmnuwmmmmeuhmmnmmnD2wrwmmwmnmlm)mahmuanammmmuAmmwmmmeuh such input here indicates whether the corresponding base station (sector): 0)howmmfie"mmmemwmhnmmmmkmmnmmflk)mdmhuflnmmcmmmhmmhsmmnwbymemmn NS I40, but the base station (sector) does not detect the target MS; (ii) is on-line and the base station (sector) detects a wireless transmission from the target MS, but the target MS does not detect the base station (sector) pilot channel signal; fiohommwamtmbmenmmnQuwfidauuflwmmaM$mdmemwnmmn6«m0hmmawbymemmuflk (iv) is on-line and the base station (sector) does not detect the target MS, the base station is not detected by the target MS; or (v) is olf—line (i.e., incapable of wireless communication with one or more l‘lSs). Note that (i)-(v) are hereinafter referred to as the “detection states." Thus. by generating an ANN for each ol a plurality of net areas (potentially overlapping), a local environmental change in the wireless signal characteristics of one net area is unlikely to affect more than a small number of adjacent or overlapping net areas. Accordingly, such local environmental changes can be reflected in that only the ANNs having net areas affected by the local change need to be retrained. Additionally, note that in cases where ill measurements from a target MS I40 are received across multiple net 70 10 15 20 25 30 CA 02265875 1999-03-09 W0 93/19337 PCT/US97/15892 areas. multiple AN Ns maybe activated, thus providing multiple MS location estimates. Further, multiple ANNs may be activated when a location signature cluster is received for a target MS I40 and location signature cluster includes location signature data corresponding to wireless transmissions between the MS and, e.g., more base stations (antenna sectors) than needed for the collection 8 described in the previous section. That is, if each collection 8 identifies four base stations l22 (antenna sectors), and a received location signature cluster includes location signature data corresponding to five base stations (antenna sectors), then there may be up to live ANNs activated to each generate a location estimate. Moreover, loreach of the smaller ANNs, it is likely that the number of input neurons is on the order of 330; (i.e, 70 inputs per each of four location signatures ( i.e., 35 inputs for the forward wireless communications and 35 for the reverse wireless communications), plus 40 additional discrete inputs for an appropriate area surrounding AW, plus l0 inputs related type of MS, power levels, etc. However, it is important to note that the number of base stations (or antenna sectors I30) having corresponding location signature data to be provided to such an ANN may vary. Thus, in some subareas of the coverage area I20, location signature data from five or more base stations (antenna sectors) may be used, whereas in other subareas three (or less) may be used. , Regarding the output from ANNs used in generating MS location estimates, there are also numerous options. In one embodiment, two values corresponding to the latitude and longitude of the target MS are estimated. Alternatively, by applying a mesh to the coverage area I20, such ANN output may be in the form of a row value and a column value of a particular mesh cell (and its corresponding area) where the target MS is estimated to be. Note that the cell sizes of the mesh need not be of a particular shape nor of uniform size. However, simple nonpblong shapes are desirable. Moreover, such cells should be sized so that each cell has an area approximately the size of the maximum degree of location precision desired. Thus, assuming square mesh cells, 250 to 350 feet per cell side in an urban/suburban area, and 500 to 700 feet per cell side in a rural area may be desirable. Artificial Neural Network Training The following are steps provide one embodiment for training a location estimating ANN according to the present invention. (a) Determine a collection, C, of clusters of RF signal measurements (i.e., location signatures) such that each cluster is for RF transmissions between an MS I40 and a common set, B, of base stations I22 (or antenna sectors I30) such the measurements are as described in (Al) above. In one embodiment, the collection C is determined by interrogating the location signature data base I320 for verified location signature clusters stored therein having such a common set B of base stations (antenna sectors). Alternatively in another embodiment, note that the collection C may be determined from (i) the existing engineering and planning data lrom service providers who are planning wireless cell sites, or (ii) service provider test data obtained using mobile test sets, access probes or other llf field measuring devices. Note that such a collection 8 of base stations (antenna sectors) should only be created when the set C of verified location signature clusters is of a sufficient size so that it is expected that the ANN can be effectively trained. (b) Determine a collection of base stations (or antenna sectors I30), 8’, from the common set B, wherein R’ is small (e.g., four or live). ‘ll 10 15 20 25 30 W0 98/ 10307 CA 02265875 1999-03-09 PCTIUS97/15892 (c) Determine the area, Am, to be associated with collection B’ of base stations (antenna sectors). in one embodiment, this area is selected by determining an area containing the set L of locations of all verified location signature clusters determined in step (a) having location signature data from each of the base stations (antenna sectors) in the collection B‘. More precisely, the area, Am, may be determined by providing a covering of the locations of L, such as, e.g., by cells of a mesh of appropriately fine mesh size so that each cell is of a size not substantially larger than the maximum HS location accuracy desired. (d) Determine an additional collection, b, of base stations that have been previously detected (and/or are likely to be detected) by at least one MS in the area Am. (e) Train the ANN on input data related to: (i) signal characteristic measurements of signal transmissions between l‘lSs I40 at verified locations in Am, and the base stations (antenna sectors) in the collection 8'. and (ii) discrete inputs of detection states from the base stations represented in the collection b. Forexample. train the ANN on input including: (i) data from verified location signatures from each of the base stations (antenna sectors) in the collection 8', wherein each location signature is part of a cluster in the collection C; (ii) a collection of discrete values corresponding to other base stations (antenna sectors) in the area b containing the area, Am. Regarding (d) immediately above, it is important to note that it is believed that less accuracy is required in training a ANN used for generating a location hypothesis (in a foil I224) forthe present invention than in most applications of ANNs (or other trainable/adaptive components) since. in most circumstances, when signal measurements are provided for locating a target MS I40, the location engine I39 will activate a plurality location hypothesis generating modules (corresponding to one or more f0l1s I224) for substantially simultaneously generating a plurality of different location estimates (i.e., hypotheses). Thus, instead of training each ANN so that it is expected to be, e.g.. 92% or higher in accuracy, it is believed that synergies with MS location estimates from other location hypothesis generating components will effectively compensate for any reduced accuracy in such a ANN (or any other location hypothesis generating component). Accordingly, it is believed that training time for such ANNs may be reduced without substantially impacting the MS locating performance of the location engine I39. Finding Near-Optimal Location Estimating Artificial Neural Networks In one traditional artificial neural network training process, a relatively tedious set of trial and error steps may be perfonned for configuring an ANN so that training produces effective learning. In particular, an ANN may require configuring parameters related to, for example, input data scaling, test/training set classification, detecting and removing unnecessary input variable selection. However, the present invention reduces this tedium. lhat is, the present invention uses mechanisms such as genetic algorithms or other mechanisms for avoiding non-optimal but locally appealing (i.e., local minimum) solutions, and locating near-optimal solutions instead. In particular. such mechanism may be used to adjust the matrix of weights for the ANNs so that very good, nearoptimal ANN configurations may be found efficiently. Furthermore, since the signal processing system I220 uses various 72 IO 20 25 30 CA 02265875 1999-03-09 wo 9s/10307 PCT/US97Il5892 types of signal processing filters for filtering the RF measurements received from transmissions between an MS I40 and one or more base stations (antenna sectors I30), such mechanisms for finding near-optimal solutions may be applied to selecting appropriate filters as well. Accordingly, in one embodiment of the present invention, such filters are paired with particular ANNs so that the location signature data supplied to each ANN is filtered according to a corresponding “filter description" for the ANN, wherein the filter description specifies the filters to be used on location signature data prior to inputting this data to the ANN. In particular, the filter description can define a pipeline of filters having a sequence of filters wherein for each two consecutive filters, f, and f, (l, preceding f,), in a filter description, the output of f, flows as input to f,. Accordingly, by encoding such a filter description together with its corresponding ANN so that the encoding can be provided to a near optimal solution finding mechanism such as a genetic algorithm. it is believed that enhanced ANN locating performance can be obtained. That is, the combined genetic codes of the filter description and the ANN are manipulated by the genetic algorithm in a search for a satisfactory solution (i.e., location error estimates within a desired range). This process and system provides a mechanism for optimizing not only the artificial neural network architecture, but also identifying a near optimal match between the ANN and one or more signal processing filters. Accordingly, the following filters may be used in a filter pipeline of a filter description: Sobel, median, mean. histogram normalization, input cropping, neighbor, Gaussian, Weiner filters. One embodiment for implementing the genetic evolving of filter description and ANN pairs is provided by the following steps that may automatically performed without substantial manual effort; I) Create an initial population of concatenated genotypes. or genetic representations for each pair of an artificial neural networks and corresponding filter description pair. Also, provide seed parameters which guide the scope and characterization of the artificial neural network architectures, filter selection and parameters, genetic parameters and system control parameters. 2) Prepare the input or training data, including, for example. any scaling and normalization of the data. 3) Build phenotypes, or artificial neural network/filter description combinations based on the genotypes. 4) Train and test the artificial neural network/filter description phenotype combinations to determine fitness; e.g., determine an aggregate location error Jneasurenient for each network/filter description phenotype. 5) Compare the fitnesses and/or errors, and retain the best network/filter description phenotypes. 6) Select the best networks/filter descriptions in the phenotype population (i.e., the combinations with small errors). 7) llepopulate the population of genotypes for the artificial neural networks and the filter descriptions back to a predetermined size using the selected phenotypes. 8) Combine the artificial neural network genotypes and filter description genotypes thereby obtaining artificial neural network/filter combination genotypes. 9) Nate the combination genotypes by exchanging genes or characteristics/features of the network/ filter combinations. I0) lf system parameter stopping criteria is not satisfied, return to step 3. 10 15 20 25 30 CA 02265875 1999-03-09 W0 93,1030-, PCT/US97I15892 Note that artificial neural network genotypes may be formed by selecting various types of artificial neural network architectures suited to function approximation, such as fast back propagation, as well as characterizing several varieties of candidate transfer/activation functions, such as Tanh, logistic, linear, sigmoid and radial basis. furthermore, ANNs having complex inputs may be selected (as determined by a filter type in the signal processing subsystem i220) for the genotypes. Examples of genetic parameters include: (a) maximum population size (typical default; 300), (b) generation limit (typical default: 50), (c) selection criteria, such as a certain percentage to survive (typical default: 0.5) or roulette wheel, (d) population refilling, such as random or cloning (default), (e) mating criteria, such as tail swapping (default) or two cut swapping, (f) rate for a choice of mutation criterion. such as random exchange (default: 0.25) or section reversal, (g) population size of the concatenated artificial neural networlt/ filter combinations, (h) use of statistical seeding on the initial population to bias the random initialization toward stronger first order relating variables, and (i) neural node influence factors, e.g., input nodes and hidden nodes. Such parameters can be used as weighting factors that influences the degree the system optimizes for accuracy versus network compactness. for example, an input node factor greater than 0 provides a means to reward artificial neural networks constructed that use fewer input variables (nodes). A reasonable default value is 0.l for both input and hidden node factors. Examples of neural net/filter description system control parameters include: (a) accuracy of modeling parameters. such as relative accuracy, R-squared, mean squared error, root mean squared error or average absolute error (default), and (b) stopping criteria parameters, such as generations run, elapsed time, best accuracy found and population convergence. locating a Mobile Station Using Artificial Neural Networks When using an artificial neural network for estimating a location of an MS I40, it is important that the artificial neural network be provided with as much accurate RF signal measurement data regarding signal transmissions between the target MS MO and the base station infrastructure as possible. In particular, assuming ANN inputs as described hereinabove, it is desirable to obtain the detection states of as many surrounding base stations as possible. Thus, whenever the location engine I39 is requested to locate a target MS I40 (and in particular in an emergency context such as an emergency 9ll call), the location center I40 automatically transmits a request to the wireless infrastructure to which the target MS is assigned for instructing the MS to raise its transmission power to full power fora short period of time (e.g., I00 milliseconds in a base station infrastructure configuration an optimized for such requests to 2 seconds in a non-optimized configuration). Note that the request for a change in the transmission power level of the target MS has a further advantage for location requests such as emergency 9| I that are initiated from the MS itself in that a first ensemble of RF signal measurements can he provided to the location engine l39 at the initial 9ll calling power level and then a second ensemble of RF signal measurements can be provided at a second higher transmission power level. Thus, in one embodiment of the present invention, an artificial neural network can be trained not only on the location signature cluster derived from either the initial wireless 9|l transmissions or the full power transmissions. but also on the differences between these two transmissions. In particular, the difference in the detection states of the discrete ANN inputs between the two transmission power levels may provide useful additional information for more accurately estimating a location of a target MS. 74 Us 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/ 15892 lt is important to note that when gathering RF signal measurements from a wireless base station network for locating l‘lSs, the network should not be overburdened with location related traffic Accordingly, note that network location data requests for data particularly useful for ANN based FOMs is generally confined to the requests to the base stations in the immediate area of a target MS I40 whose location is desired. for instance, both collections of base stations 8’ and b discussed in the context of triining an ANN are also the same collections of base stations from which MS location data would be requested. Thus, the wireless network MS location data requests are data driven in that the base stations to queried for location data (i.e., the collections B’ and b) are determined by previous RF signal measurement characteristics recorded. Accordingly, the selection of the collections B’ and bare adaptable to changes in the wireless environmental characteristics of the coverage area I20. LOCAUON SIGNATURE DATA BASE Before proceeding with a description of other levels of the present invention as described in (24.l) through (243) above, in this section further detail is provided regarding the location signature data base I320. Note that a brief description of the location signature data base was provided above indicating that this data base stores MS location data fmm verified and/or known locations (optionally with additional known environmental characteristic values) for use in enhancing current target MS location hypotheses and for comparing archived location data with location signal data obtained from a current target HS. However, the data base management system functionality incorporated into the location signature data base B20 is an important aspect of the present invention, and is therefore described in this section In particular, the data base management functionality described herein addresses a number of difficulties encountered in maintaining a large archive of signal processing data such as MS signal location data. Some of these difficulties can be described as follows: (a) in many signal processing contexts, in order to effectively utilize archived signal processing data for enhancing the perfonnance of a related signal processing application, there must be an large amount of signal related data in the archive, and this data must be adequately maintained so that as archived signal data becomes less useful to the corresponding signal processing application (i.e., the data becomes "inapplicable") its impact on the application should be correspondingly reduced. Moreover, as archive data becomes substantially inapplicable. it should be filtered fmm the archive altogether. However, the size of the data in the archive makes it prohibitive for such a pmcess to be perfonned manually, and there may be no simple or straightforward techniques for automating such impact reduction or filtering processes for inapplicable signal data; (b) it is sometimes difficult to determine the archived data to use in comparing with newly obtained signal processing application data; and (c) it is sometimes difficult to detennine a useful technique for comparing archived data with newly obtained signal processing application data. It is an aspect of the present invention that the data base management functionaficy of the location signature data base I320 addresses each of the difficulties mentioned immediately above. Forexample, regarding (a), the location signature data base is “self cleaning" in that by associating a confidence value with each loc sig in the data base and by reducing or increasing the confidences of archived verified loc sigs according to how well their signal characteristic data compares with newly received verified location signature data, the location signature data base l32O maintains a consistency with newly verified loc sigs. 10 15 20 25 30 CA 02265875 1999-03-09 W0 M10307 PCTIUS97/15392 The following data base management functional descriptions describe some of the more noteworthy functions of the location signature data base l320. Note that there are various ways that these functions may be embodied. So as to not overburden the reader here, the details for one embodiment is provided in APPENDIX C. Figs. l6a through |6c present a table providing a brief description of the attributes of the location signature data type stored in the location signature data base l320. LOCATION SIGNATURE PROGRAM DESCRIPTIONS The following program updates the random loc sigs in the location signature data base I320. ln one embodiment, this program is invoked primarily by the Signal Processing Subsystem. Update Location signature Database Program Update_l.oc_Sig_DB(new_loc__obj, selection_criteria, loc_sig__pop) P‘ This program updates Ioc sigs in the location signature data base I320. That is, this program updates, for example, at least the location information for verified random Ioc sigs residing in this data base. The general strategy here is to use information (i.e., "new_loc_obi”) received from a newly verified location (that may not yet be entered into the location signature data base) to assist in determining if the previously stored random verified loc sigs are still reasonably valid to use for. (29.l) estimating a location for a given collection (i.e., “bag") of wireless (e.g.. (DNA) location related signal characteristics received from an NS, (29.2) training (for example) adaptive location estimators (and location hypothesizing models), and (29.3) comparing with wireless signal characteristics used in generating an MS location hypothesis by one of the MS location hypothesizing models (denoted first Order Models, or, f0l1s). More precisely. since it is assumed that it is more likely that the newest location information obtained is more indicative of the wireless (CDMA) signal characteristics within some area surrounding a newly verified location than the verified loc sigs (location signatures) previously entered into the Location Signature data base, such verified Ioc sigs are compared for signal characteristic consistency with the newly verified location information (object) input here for determining whether some of these “older" data base verified loc sigs still appropriately characterize their associated location. In particular. comparisons are iteratively made here between each (target) loc sig “near" “new_|oc_obj" and a population of Ioc sigs in the location signature data base l320 (such population typically including the loc sig for “new_loc_obj) for. (29.4) adjusting a confidence factor of the target loc sig. Note that each such confidence factor is in the range [0, .j with 0 being the lowest and l being the highest. further note that a confidence factor here can be raised as well as lowered depending on how well the target Ioc sig matches or is consistent with the population of loc sigs to which it is compared. lhus, the confidence in any particular verified for sig. LS, can fluctuate with 76 20 25 30 W0 98/ 10307 CA 02265875 1999-03-09 PCT/US97/ 15892 successive invocations of this program if the input to the successive invocations are with location information geographically “near” LS. (29.5) remove older verified loc sigs from use whose confidence value is below a predetermined threshold. Note, it is intended that such predetermined thresholds be substantially automatically adjustable by periodically testing various confidence factor thresholds in a specified geographic area to determine how well the eligible data base loc sigs (for different thresholds) perform in agreeing with a number of verified loc sigs in a “loc sig test-bed”, wherein the test bed may be composed of, for example, repeatable loc sigs and recent random verified loc sigs. Note that this program may be invoked with a (verified/known) random and/or repeatable loc sig as input Furthermore, the target loc sigs to be updated may be selected lmm a particular group of lot sigs such as the random loc sigs or the repeatable loc sigs, such selection being determined according to the input parameter, “se|ection_criteria” while the comparison population may be designated with the input parameter, “loc__sig_pop”. for example, to update confidence factors of certain random loc sigs near “new__loc__obj", “selection_criteria” may be given a value indicating, “USE_liANDOM_LOC_SlGS", and “loc__sig_pop" may be given a value indicating, “USE__liEPEAlABlE_l.0C_SIGS". Thus, if in a given geographic area. the repeatable loc sigs (from. e.g.. stationary transceivers) in the area have recently been updated, then by successively providing “new_loc__obj" with a lot sig for each of these repeatable loc sigs, the stored random loc sigs can have their conlidences adjusted. Alternatively. in one embodiment of the present invention, the present function may be used for determining when it is desirable to update repeatable loc sigs in a particular area (instead of automatically and periodically updating such repeatable loc sigs). For example, by adjusting the confidence factors on repeatable loc sigs here provides a method fordetermining when repeatable loc sigs for a given area should be updated. Ihat is, for example, when the area's average confidence factor for the repeatable loc sigs drops below a given (potentially high) threshold, then the llSs that provide the repeatable loc sigs can be requested to respond with new loc sigs for updating the data base. Note, however, that the approach presented in this function assumes that the repeatable location information in the location signature data base I320 is maintained with high confidence by, for example, frequent data base updating. Thus, the random location signature data base verilied location information may be effectively compared against the repeatable loc sigs in an area. INPUT: new__|oc_obj: a data representation at least including a loc sig for an associated location about which Location Signature loc sigs are to have their confidences updated. selection_criteria: a data representation designating the loc sigs to be selected to have their confidences updated (may be defaulted). The following groups of loc sigs may be selected: “USE_RAN D0ll_LOC_SlGS" (this is the default), USE_llEPEATllBlE_LOC_$lGS", "USE_ALL_LOC_SlGS". Note that each of these selections has values for the following values associated with it (although the values may be defaulted): TI 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/US97I 15892 (a) a confidence reduction factor for reducing loc sig confidences, (b) a big error threshold for determining the errors above which are considered too big to ignore, (c) a confidence increase factor for increasing loc sig confidences, (d) a small error threshold for determining the errors below which are considered too small (i.e., good) to ignore. (e) a recent time for specifying a time period for indicating the loc sigs here considered to be "recent". loc_sig_pop: a data representation of the type of loc sig population to which the loc sigs to be updated are compared. The following values may be provided: (a) “USE All LOC SIGS IN DB”, (b) "USE ONLY REPEATABLE LOC SlGS" (this is the default), (c) “USE ONLY LDC SIGS WITH SIMILAR TlllE Of DAY” However, environmental characteristics such as: weather, traffic. season are also contemplated. Confidence Aging Program The following program reduces the confidence of verified loc sigs in the location signature data base I320 that are likely to be no longer accurate (i.e., in agreement with comparable loc sigs in the data base). If the confidence is reduced low enough, then such loc sigs are re moved from the data base. further, if for a location signature data base verified location composite entity (i.e., a collection of loc sigs for the same location and time), this entity no longer references any valid loc sigs, then it is also removed from the data base. Note that this program is invoked by “Update_Loc_Sig_DB". reduce_bad_DB_loc_sigs(loc_sig_bag , error__rec_set, big_error_threshold confidence_reduction_factor, recent_ti me) lnputs: loc_sig_bag: A collection or “bag” of loc sigs to be tested for determining if their confidence: should be lowered and/or any of these loc sigs removed. ermr_rec_set: A set of error records (objects), denoted “error__recs", providing information as to how much each loc sig in "loc_sig_bag" disagrees with comparable loc sigs in the data base. That is, mgeisg “error rec” here for each lot sig in "loc sig bag". big_error_threshold: The error threshold above which the errors are considered too big to ignore. confidence_reductio n_factor: The factor by which to reduce the confidence of loc sigs. recent_time: Time period beyond which loc sigs are no longer considered recent. Note that “recent" loc sigs (i.e., more recent than "recent_time") are not subject to the confidence reduction and filtering of this actions of this function. 78 20 25 30 CA 02265875 1999-03-09 WO 98/10307 PCT/US97/15892 Confidence Enhancement Program The following program increases the confidence of verified Location Signature loc sigs that are (seemingly) of higher accuracy (i.e., in agreement with comparable loc sigs in the location signature data base l320). Note that this program is invoked by “Update_loc_Sig_DB". increase_confidence__ol_good__DB_loc__sigs(nearby__loc__sig_bag, error_rec_set, small_error_threshold, confidence__increase_factor, recent_time); inputs: loc_sig_bag: A collection or "bag" of to be tested for detennining if their confidences should be increased. error_rec_set: A set of error records (objects), denoted “error_recs", providing information as to how much each lot sig in “loc__sig_bag” disagrees with comparable loc sigs in the location signature data base. That is, there is a “error rec" here for each loc sig in "loc sig bag". small_error_threshold: The error threshold below which the errors are considered too small to ignore. conlidence_increase_factor: The factor by which to increase the confidence of lot sigs. recent_time: Time period beyond which loc sigs are no longer considered recent. Note that "recent" loc sigs (i.e., more recent than “recent_time") are not subject to the confidence reduction and filtering of this actions of this function. Location Hypotheses Consistency Program The following program determines the consistency of location hypotheses with verified location information in the location signature data base I320. Note that in the one embodiment of the present invention, this program is invoked primarily by a module denoted the historical location reasoner I424 described sections hereinbelow. Moreover. the detailed description for this program is provided with the description of the historical location reasoner hereinbelow for completeness. DB_l.oc_Sig_Error_Fit(hypothesis, measured_|oc_sig__bag, search_criteria) /*' This function determines how well the collection of loc sigs in “measured__loc_sig_bag" fit with the loc sigs in the location signature data base I320 wherein the data base loc sigs must satisfy the criteria of the input parameter “search_criteria" and are relatively close to the MS location estimate of the location hypothesis, “hypothesis”. Input: hypothesis: MS location hypothesis; measured__loc_sig_bag: A collection of measured location signatures (“loc sigs” for short) obtained from the MS (the data structure here is an aggregation such as an array or list). Note, it is assumed that there is at most one lot sig here per Base Station in this collection. Additionally, note that the input data structure here may be a location signature cluster such as the "loc_sig__cluster" field of a location hypothesis (cf. Fig. 9). Note 10 T5 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 that variations in input data structures may be accepted here by utilization of flag or tag hits as one skilled in the art will appreciate; search_criteria: The criteria lor searching the verified location signature data base for various categories ol loc sigs. The only limitation on the types of categories that may be provided here is that. to be useful, each category should have meaningful number ol loc sigs in the location signature data base. The lollowing categories included here are illustrative, but others are contemplated: (a) “USE ALL LDC SIGS IN DB" (the default), (b) “USE ONLY REPEATABLE LOC SIGS", (c) “USE ONLY LDC SIGS WITH SIMILAR TlME OT DAY". Further categories of loc sigs close to the MS estimate of “hypothesis" contemplated are: all loc sigs lor the same season and same time of day, all loc sigs during a specific weather condition (e.g., snowing) and at the same time of day, as well as other limitations lor other environmental conditions such as trallic patterns. Note, if this parameter is NIL, then (a) is assumed. Returns: An error object (data type: "error_object") having: (a) an “error" field with a measurement of the error in the fit of the location signatures from the MS with verilied location signatures in the location signature data base I320; and (b) a “coolidence" Yield with a value indicating the perceived conlidence that is to be given to the “error" value. */ Location Signature Comparison Program The lollowing program compares: (al) loc sigs that are contained in (or derived from) the loc sigs in "target_loc‘sig__bag" with (bl) loc sigs computed from verilied loc sigs in the location signature data base l320. That is, each loc sig from (al) is compared with a corresponding loc sig from (b) to obtain a measurement of the discrepancy between the two loc sigs. In particular. assuming each of the loc sigs for “target_loc_sig_bag" correspond to the same target MS location. wherein this location is "target_loc", this program determines how well the loc sigs in “target_|oc_sig_bag" lit with a computed or estimated loc sig for the location, “target_loc" that is derived from the verilied loc sigs in the location signature data base i320. Thus, this program may be used: (a2) for determining how well the loc sigs in the location signature cluster for a target MS (“target_|oc_sig_bag") compares with lot sigs derived from verilied location signatures in the location signature data base, and (b2) for determining how consistent a given collection of loc sigs (“target_loc_sig_bag") from the location signature data base is with other loc sigs in the location signature data base. Note that in (b2) each of the one or more loc sigs in “target_lot_sig_bag" have an error computed here that can be used in determining if the loc sig is becoming inapplicable for predicting target MS locations. Determine_Location_Signature__Tit_Errors(target_loc, target_loc__sig_bag, search__area, search_criteria. output__criteria) /"* lnput: target_loc. An MS location or a location hypothesis for an MS. Note, this can be any of the following: 80 10 15 20 25 30 CA 02265875 1999-03-09 wo 9s/10307 PCTIUS97/15892 (a) An MS location hypothesis, in which case. if the hypothesis is inaccurate, then the loc sigs in “target__loc_sig_bag" are the location signature cluster lrom which this location hypothesis was derived. Note that if this location is inaccurate, then “target_loc_sig_bag" is unlikely to be similar to the comparable loc sigs derived from the loc sigs ol the location signature data base close “target_loc”; or (b) A previously verilied MS location, in which case, the loc sigs ol “target_loc_sig_bag" were the loc sigs measurements at the time they were verified. However, these loc sigs may or may not be accurate now. target_loc_sig_bag: Measured location signatures ("loc sigs" for short) obtained lrom the MS (the data structure here, bag, is an aggregation such as array or list). It is assumed that there is at least one lot sig in the bag. Further, it is assumed that there is at most one lot sig per Base Station; search_area: The representation of the geographic area surrounding “target_|oc". This parameter is used for searching the Location Signature data base lor verified loc sigs that correspond geographically to the location of an MS in “search_area; search_criteria: The criteria used in searching the location signature data base. The criteria may include the lollowing: (a) “USE ALL LOC SIGS lN DB", (b) "USE ONLY REPEATABLE LOC SIGS”. (c) “USE ONLY LOC SIGS WITH SIMILAR TIME OF DAY". However, environmental characteristics such as: weather, traffic, season are also contemplated. output_criteria: The criteria used in determining the error records to output in “error_rec_bag". The criteria here may include one ol: (a) "OUTPUT All POSSIBLE Ellll0R_RECS"; (b) “OUTPUT ERll0ll__ltECS FOR INPUT LOC SIGS ONLY". Returns: error_rec__bag: A bag ol error records or objects providing an indication of the similarity between each lot sig in “target__loc_sig_bag" and an estimated loc sig computed for “target_loc" lrom stored loc sigs in a surrounding area ol “target_loc". Thus, each error record/object in “error_rec__bag" provides a measurement of how well a lot sig (i.e., wireless signal characteristics) in “target_|oc_sig_bag" (for an associated BS and the MS at “target__loc”) correlates with an estimated loc sig between this BS and MS. Note that the estimated loc sigs are determined using verified location signatures in the Location Signature data base. Note, each error record in "error_rec_bag" includes: (a) a BS ID indicating the base station to which the error record corresponds; and (b) a error measurement (> = 0). and (c) a conlidence value (in [0, l]) indicating the confidence to be placed in the error measurement. 8| 10 15 20 25 30 CA 02265875 1999-03-09 W0 ,8,m3o7 PCT/US97/15892 Computed location Signature Program The following program receives a collection of loc sigs and computes a loc sig that is representative of the loc sigs in the collection. That is, given a collection ol loc sigs, “loc_sig_bag", wherein each loc sig is associated with the same predetermined Base Station, this program uses these loc sigs to compute a representative or estimated loc sig associated with the predetermined Base Station and associated with a predetermined MS location. "loc_for_estimation”. Thus, if the loc sigs in “loc_sig__bag" are from the verified loc sigs of the location signature data base such that each of these loc sigs also has its associated MS location relatively close to “loc__lor_estimation", then this program can compute and return a reasonable approximation of what a measured loc sig between an MS at “loc__lor_estimation" and the predetermined Base Station ought to be. This program is invoked by “Determine_Location_Signature_lit_Errors". estimate__loc__sig_from_DB(loc_for_estimation, |oc_sig_bag) Geographic Area Representation Program The following program determines and returns a representation of a geographic area about a location. “loc”, wherein: (a) the geographic area has associated MS locations lor an acceptable number (i.e., at least a determined minimal number) of verified loc sigs lrom the location signature data base, and (b) the geographical area is not too big. However, if there are not enough loc sigs in even 21 largest acceptable search area about “|oc". then this largest search area is returned. “D8_Loc_Sig__Error_fit" get_area__to_search(loc) Location signature Comparison Program This program compares two location signatures, “target__|oc__sig" and “comparison_loc_sig". both associated with the same predetermined Base Station and the same predetermined MS location (or hypothesized location). This program determines a measure of the dilference or error between the two loc sigs relative to the variability ol the verified location signatures in a collection ol loc sigs denoted the “comparison_loc_sig_bag" obtained from the location signature data base. it is assumed that “target_loc_sig", “comparison__loc_sig" and the loc sigs in “comparison__loc_sig_bag" are all associated with the same base station. This program returns an error record (object), “error_rec”, having an error or diflerence value and a confidence value lor the ermr value. Note, the signal characteristics of “target_loc_sig” and those ol "comparison_loc_sig" are not assumed to be similarly normalized (e.g., via lilters as per the filters of the Signal Processing Subsystem) prior to entering this function. It is further assumed that typically the input loc sigs satisfy the "search_criteria". This program is invoked by: the program, “Determine__l.ocation_Signature_lit_Errors", described above. get_difference_measurement(target_|oc_sig, comparison_loc_sig, comparison_|oc_sig_bag, search_area, search_criteria) Input: 82 20 25 CA 02265875 1999-03-09 WO 93/10307 PCT/US97/15892 target_|oc_sig: The loc sig to which the "error_rec” determined here is to be associated. comparison_loc_sig: The lot sig to compare with the “target__loc_sig". Note, if “comparison_loc__sig" is NIL. then this parameter has a value that corresponds to a noise level of “target_loc_sig". comparison_loc_sig_bag: The universe of loc sigs to use in determining an error measurement between “target_loc_sig" and “comparison_|oc__sig” . Note, the loc sigs in this aggregation include all loc sigs for the associated BS that are in the “search__area". search__area: A representation of the geographical area surrounding the location for all input loc sigs. This input is used for determining extra information about the search area in problematic circumstances. search__criteria: The criteria used in searching the location signature data base. The criteria may include the following: (3) “USE All LOC SIGS llf DB", (b) “USE ONLY REPEATABLE l.0C SlGS", (c) “USE ONLY LDC S IGS WITH SIMILAR TIME Of DAY However, environmental characteristics such as: weather, traffic, season are also contemplated. Detailed Description of the Hypothesis Evaluator Modules Context Adjuster Embodiments The context adjuster I326 performs the first set of potentially many adjustments to at least the confidences of location hypotheses, and in some important embodiments, both the confidences and the target MS location estimates provided by FOMs I224 may be adjusted according to previous performances of the F0lls. More particularly, as mentioned above, the context adjuster adjusts confidences so that, assuming there is a sufficient density verified location signature clusters captured in the location signature data base I320. the resulting location hypotheses output by the context adjuster I326 may be further pmcessed uniformly and substantially without concern as to differences in accuracy between the first order models from which location hypotheses originate. Accordingly, the context adjusteradjusts location hypotheses both to envimnmental factors (e.g., terrain, traffic. time of day, etc., as described in 30.! above), and to how predictable or consistent each first order model (TOM) has been at locating previous target MS's whose locations were subsequently verified. Of particular importance is the novel computational paradigm utilized herein. That is, if there is a sufficient density of previous verified MS location data stored in the location signature data base l320, then the FOM location hypotheses are used as an "index" into this data base (re, the location signature data base) for constructing new target MS I40 location estimates. A more detailed discussion of this aspect of the present invention is given hereinbelow. Accordingly, only a brief overview is provided here. Thus, since the location signature data base I320 stores previously captured MS location data incliiding: (a) clusters of MS location signature signals (see the location signature data base section for a discussion of these signals) and (b) a corresponding verified MS location, for each such cluster, from where the MS signals originated, 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 the context adjuster I326 uses newly created target MS location hypotheses output by the POW: as indexes or pointers into the location signature data base for identifying other geographical areas where the target MS MO is likely to be located based on the verified MS location data in the location signature data base. In particular, at least the following two criteria are addressed by the context adjuster I326: (32. I) Confidence values for location hypotheses are to be comparable regard less of flrst order models fmm which the location hypotheses originate. That is, the context adjuster moderates or dampens confidence value assignment distinctions or variations between first order models so that the higher the confidence of a location hypothesis, the more likely (or unlikely, if the location hypothesis indicates an area estimate where the target MS is NOT) the target MS is perceived to be in the estimated area of the location hypothesis regardless of the First Order Model fmm which the location hypothesis was output; (32.2) Confidence values for location hypotheses may be adjusted to account for current environmental characteristics such as month, day (weekday or weekend), time of day, area type (urban, rural, etc.), traffic and/or weather when comparing how accurate the first order models have previously been in determining an MS location according to such environmental characteristics. For example, in one embodiment of the present invention, such environmental characteristics are accounted for by utiliring a transmission area type scheme (as discussed in section 5.9 above) when adjusting confidence values of location hypotheses. Details regarding the use of area types for adjusting the confidences of location hypotheses and provided hereinbelow, and in particular, in APPENDIX D. Note that in satisfying the above two criteria, the context adjuster l326, at least in one em bodiment, may use heuristic (fuzty logic) rules to adjust the confidence values of location hypotheses from the first order models. Additionally, the context adjuster may also satisfy the following criteria: (33.l) The context adjuster may adjust location hypothesis confidence: due to BS failure(s). (33.2) Additionally in one embodiment, the context adjuster may have a calibration mode for at least one of: (a) calibrating the confidence values assigned by first order models to their location hypotheses outputs; (b) calibrating itself. A first embodiment of the context adjuster is discussed immediately hereinbelow and in APPENDIX D. However, the present invention also includes other embodiments of the context adjuster. A second embodiment is also described in Appendix D so as to not overburden the reader and thereby chance losing perspective of the overall invention. A description of the high level functions in an embodiment of the context adjuster I326 follows. Details regarding the implementation of these functions are provided in APPENDIX D. Also, many of the terms used hereinbelow are defined in APPENDlX D. Accordingly, the program descriptions in this section provide the reader with an overview of this first embodiment of the context adjuster I326. Context_adjuster(loc_hyp__list) 20 25 30 CA 02265875 1999-03-09 WO 98110307 PCTlUS97Il5892 This function adjusts the location hypotheses on the list, “loc_hyp_list". so that the confidence: of the location hypotheses are determined more by empirical data than default values from the first Order Models I224. That is, for each input location hypothesis, its confidence (and an MS location area estimate) may be exclusively determined here if there are enough verified location signatures available withir and/or surrounding the location hypothesis estimate. This function creates a new list of location hypotheses from the input list. “loc_hyp_list". wherein the location hypotheses on the new list are modified versions of those on the input fist. For each location hypothesis on the input list, one or more corresponding location hypotheses will he on the output list. Such corresponding output location hypotheses will differ from their associated input location hypothesis by one or more of the following: (a) the “image__area” field (see Fig. 9) may be assigned an area indicative of where the target MS is estimated to be. (h) if "image_area" is assigned, then the “confidence” field will be the confidence that the target MS is located in the area for “image_area". (c) if there are not sufficient “nearby" verified location signature clusters in the location signature data base I320 to entirely rely on a computed confidence using such verified location signature clusters, then two location hypotheses (having reduced confidences) will be returned, one having a reduced computed confidence (for “image_area") using the verified clusters in the Location Signature data base, and one being substantially the same as the associated input location hypothesis except that the confidence (for the field “area_est") is reduced to reflect the confidence in its paired location hypothesis having a computed confidence for “image_area". Note also, in some cases, the location hypotheses on the input list, may have no change to its confidence or the area to which the confidence applies. Get_adiusted_loc_hyp_|ist_for(loc_hyp) This function returns a list (or more generally. an aggregation object) of one or more location hypotheses related to the input location hypothesis, “loc_hyp". In particular, the returned location hypotheses on the list are “adjusted" versions of “loc_hyp" in that both their target MS l40 location estimates, and confidence placed in such estimates may be adjusted according to archival MS location information in the location signature data base I320. Note that the steps herein are also provided in flowchart form in Figs. 26a through 26c. RETURNS: loc_hyp_list This is a list of one or more location hypotheses related to the input “loc_hyp". Each location hypothesis on “loc_hyp__list" will typically be substantially the same as the input “|oc_hyp" except that there may now be a new target MS estimate in the field, “image_area”, and/or the confidence value may be changed to reflect information of verified location signature clusters in the location signature data base. The function, “get_adjusted__|oc__hyp__list__for,” and functions called by this function presuppose a framework or paradigm that requires some discussion as well as the defining of some terms. Note that some of the terms defined hereinbelow are illustrated in Fig. 243. Define the term the “the cluster set" to be the set of all MS location point estimates (e.g., the values of the “pt_est" field of the location hypothesis data type), for the present FOM, such that: 85 I0 15 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/U S97/ 15892 (a) these estimates are within a predetermined corresponding area (e.g., the “loc_hyp.pt_covering" being such a predetermined corresponding area. or more generally, this predetermined corresponding area is determined as a function of the distance from an initial location estimate. e.g., "loc_hyp.pt_est", from the Toll), and (b) these point estimates have verified location signature clusters in the location signature data base. Note that the predetermined corresponding area above will be denoted as the “cluster set area”. Define the term “image cluster set" (fora given First Order Model identified by “|oc_hyp.f0M__lD") to mean the set of flied location signature clusters whose MS location point estimates are in “the cluster set”. Note that an area containing the “image cluster set" will be denoted as the "image cluster set area" or simply the “image area" in some contexts. further note that the “image cluster set area” will be a “small” area encompassing the “image cluster set”. In one embodiment, the image cluster set area will be the smallest covering of cells from the mesh for the present f0l‘l that covers the convex hull of the image cluster set. Note that preferably, each cell of each mesh for each FOM is substantially contained within a single (transmission) area type. Thus, the present TOM provides the correspondences or mapping between elements of the cluster set and elements of the image cluster set. confidence_adj uster(T0l1_lD, image_area, image_cluster_set) This function returns a confidence value indicative of the target MS I40 being in the area for "image_area". Note that the steps for this function are provided in flowchart form in figs. 273 and 27b. RETURNS: A confidence value. This is a value indicative of the target MS being located in the area represented by “image_area” (when it is assumed that for the related “|oc_hyp," the ‘'cluster set area” is the “loc_hyp.pt_covering" and "loc_hyp.F0l‘l_lD" is “f0l'l__lD"). The function. “confidence_adjuster." (and functions called by this function) presuppose a framework or paradigm that requires some discussion as well as the defining of terms. Define the term "mapped cluster density" to be the number of the verified location signature clusters in an "image cluster set" per unit of area in the “image cluster set area". It is believed that the higher the “mapped cluster density", the greater the confidence can be had that a target MS actually resides in the "image cluster set area" when an estimate for the target MS (by the present TOM) is in the corresponding “the cluster set". Thus, the mapped cluster density becomes an important factor in determining a confidence value for an estimated area of a target l’|S such as, for example, the area represented by "image_area". However, the mapped cluster density value requires modification before it can be utilized in the confidence calculation. ln particular, confidence values must be in the range [-l , l] and a mapped cluster density does not have this constraint. Thus, a “relativized mapped cluster density" for an estimated MS area is desired, wherein this relativized measurement is in the range [-l, + l], and in particular, for positive confidence: in the 86 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 range [0, l]. Accordingly, to alleviate this difficulty. for the FOM define the term "prediction mapped cluster density" as a mapped cluster density value, MED, for the FOM and image cluster set area wherein: (i) MCD is sufficiently high so that it correlates (at least at a predetermined likelihood threshold level) with the actual target MS location being in the “image cluster set area" when a follltarget MS location estimate is in the corresponding “cluster set area"; That is, for a cluster set area (e.g., “loc_hyp.pt_covering”) for the present POM. if the image cluster set area: has a mapped cluster density greater than the “prediction mapped clusterdensity”. then there is a high likelihood of the target MS being in the image cluster set area. lt is believed that the prediction mapped cluster density will typically be dependent on one or more area types. In particular. it is assumed that for each area type, there is a likely range of prediction mapped cluster density values that is substantially uniform across the area type. Accordingly, as discussed in detail hereinbelow, to calculate a prediction mapped cluster density for a particular area type, an estimate is made of the correlation between the mapped cluster densities of image areas (lrom cluster set areas) and the likelihood that if a verified MS location: (a) has a corresponding TOM MS estimate in the cluster set. and (b) is also in the particular area type, then the verified MS location is also in the image area. Thus, if an area is within a single area type, then such a "relativized mapped cluster density" measurement for the area may be obtained by dividing the mapped cluster density by the prediction mapped cluster density and taking the smaller of: the resulting ratio and L0 as the value lor the relativized mapped cluster density. In some (perhaps most) cases. however, an area (e.g., an image cluster set area) may have portions in a number of area types. Accordingly, a "composite prediction mapped cluster density" may be computed, wherein. a weighted sum is computed of the prediction mapped cluster densities forthe portions of the area that is in each of the area types. That is, the weighting, for each of the single area type prediction mapped cluster densities. is the fraction of the total area that this area type is. Thus. a “relativized composite mapped cluster density" for the area here may also be computed by dividing the mapped cluster density by the composite prediction mapped cluster density and taking the smaller of: the resulting ratio and l.0 as the value for the relativized composite mapped cluster density. Accordingly, note that as such a relativized (composite) mapped cluster density for an image cluster set area increases/decreases. it is assumed that the confidence of the target MS being in the image cluster set area should increase/decrease. respectively. get_composite_prediction_mapped_cluster_density_lor_high_certainty(FOM__lD, image_area); The present function determines a composite prediction mapped cluster density by determining a composite prediction mapped cluster density for the area represented by “image_area" and forthe first Order Model identified by “fOM_lD". OUTPUT: composite_mapped__density This is a record for the composite prediction mapped cluster density. In particular, there are with two fields: CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 (i) a “value" field giving an approximation to the prediction mapped cluster density for the first Order Model having id, T0ll_lD; (ii) a “reliability” field giving an indication as to the reliability of the “value” field. The reliability field is in the range [0, l] with 0 indicating that the “value” field is worthless and the larger the value the more assurance can be put in "value" with maximal assurance indicated when “reliability" is I. get_prediction_mapped_cluster_density_lor(TOM_lD, area_type) The present function determines an approximation to a prediction mapped cluster density. D, loran area type such that if an image cluster set area has a mapped cluster density > = D, then there is a high expectation that the target MS I40 is in the image cluster set area. Note that there are a number of embodiments that may be utilited for this function. The steps herein are also provided in flowchart form in Figs. 293 through 29h. OUTPUT: prediction_mapped_cluster_density This is a value giving an approximation to the prediction mapped cluster density for the first Order Model having identity, "F0l’1_lD", and for the area type represented by "area_type" *'/ ft is important to note that the computation here for the prediction mapped cluster density may be more intense than sonre other computations but the cluster densities computed here need not be perfonned in real time target MS location processing. That is, the steps of this function may be performed only periodically (e.g., once a week), for each TOM and each area type thereby precomputing the output for this function. Accordingly, the values obtained here may be stored in a table that is accessed during real time target MS location processing. However, for simplicity. only the periodically performed steps are presented here. However, one skilled in the art will understand that with sufficiently fast computational devices. some related variations of this function may be performed in real-time. In particular, instead of supplying area type as an input to this function, a particular area, A, may be provided such as the image area for a cluster set area, or, the portion of such an image area in a particular area type. Accordingly, wherever "area_type" is used in a statement of the embodiment of this function below, a comparable statement with ‘‘A’' can be provided. Location Hypothesis Analyzer Embodiment Referring now to Fig. 7,an embodiment of the Hypothesis llnalyter is illustrated. The control component is denoted the control module I400 . Thus, this control module manages or controls access to the run time location hypothesis stoiage area Mill. The control module l400 and the run time location hypothesis storage area l4l0 may be implemented as a blackboard system and/oran expert system. Accordingly. in the blackboard embodiment, ,and the control module I400 determines when new location hypotheses may be entered onto the 88 20 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97llS892 blackboard fmm other processes such as the context adjuster I326 as well as when location hypotheses may be output to the most likehhood estimator I344. Tlte following is a brief description of each submodule included in the location hypothesis analyzer I332. (35.l) A control module I400 for managing or controlling further processing of location hypotheses received lmm the context adjuster. This module controls all location hypothesis processing within the location hypothesis analyzer as well as providing the input interface with the oontext adjuster. There are numerous embodiments that may be utilized for this module, including, but not limited to, expert systems and blackboard managers. (35.2) A run-time location hypothesis storage area l4l0 for retaining location hypothesesduring their processing by the location hypotheses analyzer. This can be, for example, an expert system fact base ora blackboard. Note that in some of the discussion hereinbelow, for simplicity, this module is referred to as a “blackboard”. However, it is not intended that such notation be a limitation on the present invention; i.e., the term "blackboard" hereinafter will denote a runtime data repository for a data processing paradigm wherein the flow of control is substantially data-driven. (35.3) An analytical reasoner module l4l6 lordetermining if (or how well) location hypotheses are consistent with well known physical or heuristic constraints as, e.g., mentioned in (30.4) above. Note that this module may be a daemon or expert system rule base. (35.4) An historical location reasoner module l424 for adjusting location hypotheses’ conlidences according to how well the location signature characteristics fi.e., loc sigs) associated with a location hypothesis compare with “nearby" loc sigs in the location signature data base as indicated in (303) above. Note that this module may also be a daemon or expert system rule base. (35.5) A location extrapolator module I432 for use in updating previous location estimates for a target MS when a more recent location hypothesis is provided to the location hypothesis analyzer l332. That is, assume that the control module I400 receives a new location hypothesis for a target MS for which there are also one or more previous location hypotheses that either have been recently processed (Le. they reside in the MS status repository I338, as shown best in Fig. 6), or are curremly being processed (i.e., they reside in the run- time location hypothesis storage area l4l0). Accordingly, if the active__timestamp (see fig. 9 regarding location hypothesis data lields) of the newly received location hypothesis is sufficiently more recent than the active__timestamp of one of these previous location hypotheses. then an extrapolation may be performed by the location extrapolator module I432 on such previous location hypotheses so that all target MS location hypotheses being concurrently analyzed are presumed to include target MS location estimates for substantially the same point in time. Thus, initial location estimates generated by the f0lfs using different wireless signal measurements, from dillerem signal transmission time intervals, may have their corresponding dependent location hypotheses utihzed simultaneously for determining a most hkely target MS location estimate. Note that this module may also be daemon or expert system mle base. (35.6) hypothesis generating module l 428 for generating additional location hypotheses according to, for example, MS location information not adequately utilized or modeled. llote. location hypotheses may also be decomposed here if, for example it is determined that a location hypothesis includes an MS area estimate that has subareas with radically dillerent characteristics such as an MS area estimate that includes an uninhabited area and a densely populated area Additionally. the hypothesis generating module l428 may generate “poor reception” location hypotheses that specily MS location areas of known poor reception that are “near" or intersect curremly 89 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT M897! 1 5892 active location hypotheses. Note, thatthese poor reception location hypotheses may be specially tagged (e.g., with a distinctive fOl‘l__lD value or specific tag field) so that regardless of substantially any other location hypothesis confidence value overlapping such a poor reception area, such an area will maintain a confidence value of “unknown” (i.e.. zero). Note that substantially the only exception to this constraint is location hypotheses generated from mobile base stations I48. Note that this module may also be daemon or expert system rule base. ln the blackboard system embodiment of the location hypothesis analyzer,a blackboard system is the mechanism by which the last adjustments are performed on location hypotheses and by which additional location hypotheses may be generated. Briefly, a blackboard system can be described as a particular class of software that typically includes at least three basic components. That is: (36.l) a data base called the "blackboard." whose stored inlomration is commonly available to a collection of programming elements known as "daemons", wherein, in the present invention, the blackboard includes infonnation concerning the current status of the location hypotheses being evaluated to determine a "most likely" MS location estimate. Note that this data base is provided by the run time location hypothesis storage area l4l0; (36.2) one or more active (and typically opportunistic) knowledge sources, denoted conventionally as “daemons,” that create and modify the contents of the blackboard. The blackboard system employed requires only that the daemons have application knowledge specific to the MS location pmblem addressed by the present invention. As shown in fig. 7, the knowledge sources or daemons in the hypothesis analyzer include the analytical reasoner module |4l6, the hypothesis generating module I428, and the historical location reasoner module l4|6; (363) a control module that enables the reafrtation of the behavior in a serial computing environment. The control element orchestrate: the flow of control between the various daemons. This control module is pmvided by the contml module I400. Note that this blackboard system may be commercial, however, the knowledge sources, i.e., daemons, have been developed specifically for the present invention. for further information regarding such blackboard systems. the following references are incorporated herein by reference: (a) Jagannathan, ll., Dodhiawala. K, & Baum, L S. (I989). Blackboard architectures and applications Boston, MA: Harcourt Brace Jovanovich Publishers; (b) Engelmore. ll.. & Morgan. T. (I988). Blackboard systems. Reading, MA: Addison—Wesley Publishing Company. Alternatively, the contml module I400 and the run-time location hypothesis storage area l4l0 may be implemented as an expert system or as a fuzzy rule inferencing system. wherein the control module I400 activates or “lires" rules related to the knowledge domain (in the present case. rules relating to the accuracy of MS location hypothesis estimates), and wherein the mles provide a computational embodiment of. for example, constraints and heuristics related to the accuracy of MS location estimates. Thus, the contml module l400 for the present embodiment is also used for orchestrating. coordinating and commlling the activity of the individual rule bases of the location hypothesis analyter (e.g. as shown in fig. 7, the analytical reasoner module l4l6, the hypothesis generating module l 428 , the historical location reasoner module I424. and the location extrapolator module M32). for further information regarding such expert systems, the following reference is incorporated herein by reference: Watemtan, D. A. (I970). A guide to expert systems. Reading. MA: Addison-Wesley Publishing Company. 90 20 25 30 CA 02265875 1999-03-09 wo 98/10307 PCTIUS97/15892 MS Status Repository Embodiment The MS status repository I338 is a mn-time storage manager for storing location hypotheses from previous activations of the location engine I39 (as well as the output target MS location estimate(s)) so that a target MS may be tracked using target MS location hypotheses from previous location engine l39 activations to determine, for example, a movement of the target HS between evaluations of the target MS location. Thus, by retaining a moving window of previous location hypotheses used in evaluating positions of a target MS, measurements of the target MS’s velocity, acceleration, and likely next position may be determined by the location hypothesis analyzer I332. Further, by providing accessibility to recent MS location hypotheses, these hypotheses may be used to resolve conflicts between hypotheses in a current activation for locating the target MS; e.g., MS paths may be stored here for use in extrapolating a new location Most Likelihood Estimator Embodiment The most likefihood estimator I344 is a module for determining a "most likely” location estimate for a target MS I40 being located (e.g., as in (30.7) above). In one embodiment, the most likelihood estimator performs an integration or summing of all location hypothesis confidence values for any geographic region(s) of interest having at least one location hypothesis that has been provided to the most likefihood estimator, and wherein the location hypothesis has a relatively (or sufficiently) high confidence. That is, the most likelihood estimator B44 detennines the area(s) within each such region having high confidences (or confidences above a threshold) as the most fikely target MS I40 location estimates. In one embodiment of the most likelihood estimator I344, this module utilizes an area mesh. ll, over which to integrate, wherein the mesh cells of M are preferably smaller than the greatest location accuracy desired. That is, each cell, c, of ll is assigned a confidence value indicating a likelihood that the target MS I40 is located in c, wherein the confidence value for c is detennined by the confidence values of the target MS location estimates provided to the most likelihood estimator I344. Thus, to obtain the most likely location determination(s) the following steps are performed: (a) For each of the active location hypotheses output by, e.g., the hypothesis analyzer I332 (alternatively. the context adjuster l326), each corresponding MS location area estimate, LAE, is provided with a smallest covering, CL“, of cells c lmm ll. (li) Subsequently, each of the cells of Cu, have their confidence values adjusted by adding to it the confidence value for ME. Accordingly, if the confidence of [EA is positive, then the cells of C”, have their confidences increased. Alternatively, if the confidence of [EA is negative, then the cells of Cm have their confiderices decreased. (c) Given that the interval [-l.0, + |.0] represents the range in confidence values, and drat this range has been partitioned into intervals, lnt, having lengths of, eg., 0.05, for each interval, lnt, perlonn a cluster analysis function for clustering cells with confidences that are in lnt. Thus, a topographical-type map may be constructed lmm the resulting cell clusters, wherein higher confidence areas are analogous to representations of areas having higher elevations. (cl) Output a representation of the resulting clusters for each lnt to the output gateway I356 for determining the location granularity and representation desired by each location application I46 requesting the location of the target MS l 40. 9! CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 Of course, variations in the above algorithm also within the scope of the present invention. for example. some embodiments of the most likelihood estimator I344 may: (e) Perform special processing for areas designated as "poor reception" areas. For example, the most likelihood estimator I344 may 3 be able to impose a confidence value of zem (i.e., meaning it is unknown as to whether the target MS is in the area) on each such poor reception area regardless of the location estimate confidence values unless there is a location hypothesis from a reliable and unanticipated source. ihat is, the mesh cells of a poor reception area may have their confrdences set to zero unless,e.g., there is a location hypothesis derived fmm target MS location data pmvided by a nrobile base station I48 that: (a) is near the poor reception area, (b) able to detect that the target MS I40 is in the poor reception area, and (t) can relay 10 target MS location data to the location center I42. In such a case, the confidence of the target MS location estimate from the MBS location hypothesis may take precedence. (l) Additionally, in some embodiments of the most likelihood estimator I344, cells c of if that are "near" or adjacent to a covering Cm may also have their confidence: adjusted according to how near the cells c are to the covering. That is. the assigning of confidences to cell meshes may be "fuzzifred" in the terms of fuzzy logic so that the confidence value of each location 15 hypothesis utilized by the most likelihood estimator I344 is provided with a weighting factor depending on its priority to the target MS location estimate of the location hypothesis More precisely. it is believed that “neamess," in the present comext, should be monotonic with the “wideness” of the covering; i.e., as the extent of the covering increases (decreases) in a particular direction, the cells c affected beyond the covering also increases (decreases). furthermore, in some embodiments of the most likelihood estimator I344, the greater (lesser) the confidence in the LEA, the more (lewer) cells c beyond the covering 20 have their confrdences affected. To describe this technique in further detail, reference is made to fig. l0. wherein an area A is assumed to be a covering Cm having a confidence denoted “conl”. Accordingly, to determine a confidence adjustment to add to a cell c not in A (and additionally, the cemroid of A not being substantially identical with the centroid of c which could occur if A were donut shaped). the following steps may be performed: (i) Determine the centmid of A, denoted Cent(A). 25 (ii) Determine the centmid of the cell C. denoted 0. (iii) Determine the extent of A along the line between Cent(A) and Q, denoted L (iv) Fora given type of probability density function, P(x), such as a Gaussian function. let T be the beginning portion of the function that lives on the x-axis interval [0, t].wherein P(t) = ABS(conl) = the absolute value of the confidence of Cm. 30 (v) Stretch I along the x-axis so that the stretched function, denoted sT(x), has an x-axis support of [0, L/(l + e" Mwmfli '”)],where a is in range of 3.0 to l0.D; e.g., 5Ø Note that sT(x) is the function. P(x * (l +e'['(m‘°"”' 1”)/L), on this stretched extent. further note that for confidence: of + l and -l,the support of slot) is [0, L] and for confidence: at (or near) zero this support. further. the term. W +e-rauss<wn- In) 92 CA 02265875 1999-03-09 W0 M10307 PCT/US97/15892 is monotonically increasing with L and ABS(conf). (vi) Determine D = the minimum distance that Q is outside of it along the line between Cent(A) and 0. (vii) Determine the absolute value of the change in the confidence of c as sT(D). (viii) Provide the value sT(D) with the same sign as conl, and provide the potentially sign changed value sT(D) as 5 the confidence of the cell c. Additionally, in some embodiments, the most likelihood estimator 1344, upon receiving one or more location hypotheses fmm the hypothesis analyzer I332, also performs some or all of the following taslrs: (37.l) Filters out location hypotheses having confidence values near zero whenever such location hypotheses are deemed too unreliable to be utilized in determining a target MS location estimate. For example, location hypotheses having confidence to values in the range [-0.02, 0.02] may be filtered here; (37.2) Determines the area of interest over which to perform the integration. ln one embodiment, this area is a convex hull including each of the MS area estimates fmm the received location hypotheses (wherein such location hypotheses have not been removed fmm consideration by the filtering process of (37.l)); (373) Determines, once the integration is perlomied, one or more collections of contiguous area mesh cells that may be deemed a 15 “most likely” MS location estimate, wherein each such collection includes one or more area mesh cells having a high confidence value. Detailed Description of the Location Hypothesis Analyzer Submodules Analytical lleasoner Module The analytical reasoner applies constraint or "sanity" checks to the target MS estimates of the location hypotheses residing in the Run-time 20 Location Hypothesis Storage Area for adjusting the associated confidence values accordingly. In one embodiment, these sanity checks involve "path" infonnation. That is, this module detennines if (or how well) location hypotheses are consistent with well known physical constraints such as the laws of physics, in an area in which the MS (associated with the location hypothesis) is estimated to be located. for example, if the difference between a previous (most likely) location estimate of a target MS and an estimate by a current location hypothesis requires the MS to: 25 (a) move at an unreasonably high rate of speed (e.g., 200 mph), or (b) move at an unreasonably high rate of speed loran area (e.g., 80 mph in acorn patch), or (c) make unreasonably sharp velocity changes (e.g., fmm 60 mph in one direction to 60 mph in the opposite direction in 4 sec), then the confidence in the current hypothesis is reduced. Such path infomiation may be derived for each time series of location hypotheses resulting from the FOMs by maintaining a window of previous location hypotheses in the MS status repository I338. Moreover, by additionally 30 retaining the “most likely" target MS location estimates (output by the most likelihood estimator B44), current location hypotheses may be compared against such most likely MS location estimates 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/U S97/ 15892 The following path sanity checks are incorporated into the computations of this module. That is: (I) do the predicted MS paths generally follow a known transportation pathway (e.g., in the case of a calculated speed of greater than 50 miles per hour are the target MS location estimates within, for example. .2 miles of a pathway where such speed may be sustained); if so (not), then increase (decrease) the confidence of the location hypotheses not satisfying this criterion; (2) are the speeds, velocities and accelerations, determined from the current and past target MS location estimates, reasonable for the region (e.g, speeds should be less than 60 miles per hour in a dense urban area at 9 am); if so (not). then increase (decrease) the confidence of those tint are (un)reasonable; (3) are the locations, speeds, velocities and/or accelerations similar between target MS tracks produced by different f0Ms similar, decrease the confidence of the currently active location hypotheses that are indicated as “outliers" by this criterion; (4) are the currently active location hypothesis target HS estimates consistent with previous predictions of where the target MS is predicted to be from a previous (most likely) target MS estimate; if not. then decrease the confidence of at least those location hypothesis estimates that are substantially different from the corresponding predictions. Note, however, that in some cases this may be over mled. For example, if the prediction is for an area for which there is Location Base Station coverage, and no Location Base Station covering the area subsequently reports communicating with the target MS, then the predictions are incorrect and any current location hypothesis from the same POM should not be decreased here if it is outside of this Location Base Station coverage area. Notice from Fig. 7 that the analytical reasoner can access location hypotheses currently posted on the llun~time Location Hypothesis Storage Area. Additionally, it interacts with the Pathway Database which contains information concerning the location of natural transportation pathways in the region (highways, rivers, etc.) and the Area Characteristics Database which contains infonnation concerning, for example, reasonable velocities that can be expected in various regions (for instance, speeds of 80 mph would not be reasonably expected in dense urban areas). Note that both speed and direction can be important constraints; e.g, even though a speed might be appropriate loran area, such as 20 mph in a dense urban area, if the direction indicated by a time series of related location hypotheses is directly through an extensive building complex having no through traffic routes, then a reduction in the confidence of one or more of the location hypotheses may be appropriate. One embodiment of the Analytical Reasoner illustrating how such constraints may be implemented is provided in the following section. Note, however, that this embodiment analyzes only location hypotheses having a nonnegative confidence value. Modules of an embodiment of the analytical reasoner module |4l6 are provided hereinbelow. Path Comparison Module The path comparison module I454 implements the following strategy: the confidence of a particular location hypothesis is be increased (decreased) if it is (not) predicting a path that lies along a known transportation pathway (and the speed of the target MS is sufficiently high). For instance. if a time series of target NS location hypotheses fora given FOM is predicting a path of the target MS that lies along an interstate 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 highway, the confidence of the currently active location hypothesis lorthis TOM should, in general, be increased. Thus. at a high level the following steps may be perfonned: (a) For each TOM having a curremly active location hypothesis in the Run-time location Hypothesis Storage Area (also denoted “blackboard"), detennine a recent "path" obtained from a time series of location hypotheses for the TOM. This computation lor the ‘ "path" is perfomted by stringing together successive "oenter ol area” (COA) or centroid values determined from the most pertinent target MS location estimate in each location hypothesis (recall that each location hypothesis may have a plurality of target MS area estimates with one being the most pertinent). The inlormation is stored in, for example, a matrix of values wherein one dimension of the matrix identities the FOM and the a second dimension of the matrix represents a series of (DA path values. Olcourse, some entries in the matrix may be undefined. (b) Compare each path obtained in (a) against known transportation pathways in an area containing the path. A value, path_match(i), representing to what extem the path matches any known transportation pathway is computed- Such values are used later in a computation for adjusting the confidence ol each corresponding cunemly active location hypothesis. Velocity/Acceleration Calculation Module The velocity/acceleration calculation module I458 computes velocity and/or acceleration estimates lor the target MS MO using currently active location hypotheses and previous location hypothesis estimates of the target HS. In one embodiment, for each TOM I224 having a cunently active location hypothesis (with positive conlidences) and a sufficient numberol previous (reasonably recent) target MS location hypotheses. a velocity and/or acceleration may be calculated. In an alternative embodiment, such a velocity and/or acceleration may be calculated using the currently active location hypotheses and one or more recent “most likely" locations of the target MS output by the location engine l39. If the estimated velocity and/or acceleration conesponding to a cunemly active location hypothesis is reasonable forthe region, then its conlidence value may be incremented; if not, then its conlidence may be decrememed. The algorithm may be summarized as lollows: (a) Approximate speed and/or acceleration estimates for currently active target MS location hypotheses may be pmvided using path infomration related to the currently active location hypotheses and previous target MS location estimates in a manner similar to the description of the path comparison module I454. Accordingly. a single confidence adjustment value may be determined for each currently active location hypothesis for indicating the extent to which its corresponding velocity and/or acceleration calculations are reasonable for its particular target MS location estimate. This calculation is perlonned by retrieving information from the area characteristics data base I450 (e.g., Figs. 6 and 7). Since each location hypothesis includes timestamp data indicating when the MS location signals were received from the target MS, the velocity and/or acceleration associated with a path for a currently active location hypothesis can be straightforwardly appmximated. Accordingly. a confidence adjustment value, vel_ok(i), indicating a likelihood that the velocity calculated for the i‘" currently active location hypothesis (having adequate corresponding path inlonnation) may be appropriate is calculated using forthe environmental characteristics of the location hypothesis‘ target MS location estimate. For example, the area characteristic data base M50 may include expected maximum velocities and/or accelerations for each area type and/or cell of a cell mesh of the coverage area I20. Thus. velocities and/or accelerations above such maximum values may be indicative of anomalies in the MS location estimating process. Accordingly, in one embodiment, the most 95 20 25 30 CA 02265875 1999-03-09 “,0 93,1o3o7 PCT/US97/15892 recent location hypotheses yielding such extreme velocities and/or accelerations may have theirconftdence values decreased. for example, if the target MS location estimate includes a portion of an interstate highway, then an appropriate velocity might correspond to a speed of up to l00 miles per hour, whereas if the target HS location estimate includes only rural dirt roads and tomato patches, then a likely speed might be no more than 30 miles per hour with an maximum speed of 60 miles per hour (assuming favorable envimnniental characteristics such as weather). Note that a list of such environmental characteristics may include such factors as: area type, time of day, season. further note that more unpredictable environmental characteristics sudr as traffic flow patterns, weather (e.g, clear, mining, snowing, etc) may also be included, values for these latter characteristics coming fmm the environmental data base I354 which receives and maintains information on such unpredictable characteristics (e.g., figs 6 and 7). Also note that a similar confidence adjustment value, acc_ok(i), may be provided for currently active location hypotheses. wherein the confidence adjustment is related to the appropriateness of the acceleration estimate of the target MS. Attribute Comparison Module The attribute comparison module I462 compares attribute values for location hypotheses generated from different mils. and determines if the confidence of certain of the currently active location hypotheses should be increased due to a similarity in related values for the attribute. That is, for an attribute A, an attribute value for A derived from a set SW.) of one or more location hypotheses generated by one TOM. f0lf{l] is compared with another attribute value for A derived fmm a set Sm of one or more location hypotheses generated by a different FOM, FOM [2] for detenniriing if these attribute values cluster (ie., are sufficiemly close to one another) so that a currently active location hypothesis in S,o,,[,] and a currently active location hypothesis in Smm should have their conlidences increased. for example, the attribute may be a “target MS path data” attribute, wherein a value for the attribute is an estimated target MS path derived from location hypotheses generated by a fixed TON over some (recent) time period. Alternatively, the attribute might be. for example, one of a velocity and/or acceleration, wherein a value for the attribute is a velocity and/or acceleration derived from location hypotheses generated by a fixed TOM over some (recent) time period. ln a general comext, the attribute comparison module l462 operates according to the following premise: (38.l) for each of two or more currently active location hypotheses (with. e.g., positive confdences) if: (a) each of these cunently active location hypotheses, H, was initially generated by a corresponding different TOM"; (b) for a given MS estimate attribute and each such currently active location hypothesis, H, there is a corresponding value for the attribute (e.g., the attribute value might be an MS path estimate, or alternatively an MS estimated velocity, or an MS estinrated acceleration), wherein the attribute value is derived without using a FOM different fmm f0fl,j. and; (c) the derived attribute values cluster sufficiently well, then each of these currently active location hypotheses, H, will have their corresponding conlidences increased. That is, these confidence: will be increased by a confidence adjustment value or delta. Note that the phrase "cluster sufficiently well" above may have a number of technical embodiments, including performing various cluster analysis techniques wherein any clusters (according to some statistic) must satisfy a system set threshold for the members of the cluster being 96 Us 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 close enough to one another. Further, upon determining the (any) location hypotheses satisfying (38.l ), there are various techniques that may be used in detennining a change or delta in conlidences to be applied. for example, in one embodiment, an initial default confidence delta that may be utilized is: if “cf" denotes the oonfidence of such a cunently active location hypothesis satisfying (38.l), then an increased confidence that still remains in the interval [0, l.0] may be:cl + [(l - cf)/(I + cf )]Z.or, cf * [l.0 + cl "1, n.= >2, or,cf * [a constant having a system tuned parameteras a factor]. That is, the confidence deltas for these examples are: [(l - cl)/(l + cf )]7 (an additive delta), and, [l.0 + cl "] (a multiplicative delta), and a constant. Additionally, note that it is within the scope of the present invention to also provide such confidence deltas (additive deltas or multiphcative deltas) with factors related to the number of such location hypotheses in the cluster. Moreover, note that it is an aspect of the present invention to provide an adaptive mechanism (re. the adaptation engine I382 shown in Figs. 5, 6 and 8) for automatically detennining perfomiance enhancing changes in confidence adjustment values such as the confidence deltas for the present module. ihat is. sudt changes are determined by applying an adaptive mechanism. such as a genetic algorithm, to a collection of “system parameters” (including parameters specifying confidence adjustment values as well as system parameters of, for example, the context adjuster I326) in order to enhance perlomiance of the present invention. More particularly, such an adaptive mechanism may repeatedly perform the following steps: (a) modify such system parameters; (b) consequently activate an instantiation of the location engine I39 (having the modified system parameters) to process. as input, a series of MS signal location data that has been archived together with data corresponding to a verified HS location from which signal location data was transmitted (e.g, such data as is stored in the location signature data base I320); and (c) then determine if the modifications to the system parameters enhanced location engine I39 performance in comparison to previous performances. Assuming this module adjusts confidences of currently active location hypotheses according to one or more of the attributes: target MS path data, target MS velocity, and target MS acceleration, the computation for this module may be summarized in the following steps: (a) Determine if any of the currently active location hypotheses satisfy the premise (3B.l) for the attribute. Note that in making this detennination, average distances and average standard deviations for the paths (velocities and/or accelerations) corresponding to currently active location hypotheses may be computed- (b) For each currently active location hypothesis (wherein “i” uniquely identifies the location hypothesis) selected to have its confidence increased, a confidence adjustment value, path_similar(i) (alternatively, velocity_similar(i) and/or acce|eration_similar(i) ), is computed indicating the extent to which the attribute value matches another attribute value being predicted by another FOM. Note that such confidence adjustment values are used later in the calculation of an aggregate confidence adjustment to particular ciinently active location hypotheses. Analytical Reasoner Controller Given one or more currently active location hypotheses for the same target MS input to the analytical reasoner controller I466, this contmller activates. for each such input location hypothesis, the other submodules of the analytical reasoner module l4l6 (denoted hereinafter as “adjustment submodules”) with this location hypothesis. Subsequently, the analytical reasoner contmller I466 receives an output confidence 97 20 25 30 W0 98/10307 CA 02265875 1999-03-09 PCT/US97/1 5892 adjustment value computed by each adjustment submodule for adjusting the confidence of this location hypothesis. Note that each adjustment submodule determines: (a) whether the adjusunent submodule may appropriately compute a confidence adjustment value for the location hypothesis supplied by the controller. (For example, in some cases there may not be a sulficient number of location hypotheses in a time series from a fixed POM); (b) if appropriate, then the adjustment submodule computes a non-zero confidence adjustment value that is returned to the analytical reasoner controller. Subsequently, the controller uses the output from the adjustment submoduies to compute an aggregate confidence adjustment lorthe corresponding location hypothesis. In one particular embodiment of the present invention, values for the eight types of confidence adjustment values (described in sections above) are output to the present controller for computing an aggregate confidence adjustment value for adjusting the confidence of the currently active location hypothesis presently being analyzed by the ariaiytical reasoner module l4l6. As an example of how such confidence adjustment values may be utilized. assuming a currently active location hypothesis is identified by "i", the outputs lmm the above described adjustniem subniodules may be more fully described as: path_match(i) vel_ok(i) acc_ok(0 similar _path(i) velocity_similar(i) I if there are sulficient previous (and recent) location hypotheses for the same target MS as “i” that have been generated by the same f0ll that generated “i”. and, the target MS location estimates provided by the location hypothesis “i" and the previous location hypotheses follow a known transportation pathway. 0 otherwise. I if the velocity calculated for the i"' currently active location hypothesis (assuming adequate corresponding path inlomtation) is typical for the area (and the current environrriental characteristics) of this location hypothesis’ target MS location estimate; 01 il the velocity calculated lorthe i"' cunently active location hypothesis is near a maximum for the area (and the current environmental characteristics) of this location hypothesis‘ target MS location estirnate;. 0 if the velocity calculated is above the rriaximum. I if the acceleration calculated for the it" currently active location hypothesis (assuming adequate corresponding path infomiation) is typical forthe area (and the current environmental characteristics) of this location hypothesis’ target MS location estimate; 0.2 if the acceleration calculated lorthe i"' cunently active location hypothesis is near a maximum for the area (and the current environmental characteristics) of this location hypothesis’ target MS location estimate;. 0 if the acceleration calculated is above the maximum I if the location hypothesis "i" satisfies (38.l) for the target MS path data attribute; 0 otherwise. I if the location hypothesis “i” satisfies (38.l) for the target MS velocity attribute: 0 otherwise. 98 20 25 30 - -09 CA 02265875 1939 03‘ W0 93,1030-7 PCT/US97/15892 if the location hypothesis “i" satisfies (38,!) for the target MS acceleration attribute; 0 otherwise. if the location hypothesis “i" is "near" a previously predicted MS location for the target HS; 0 otherwise. acceleration_similar(0 l extrapolation_chk(i) l Additionally, foreach of the above confidence adjustments, there is a corresponding location engine l39 system setable parameter whose value may be determined by repeated activation of the adaptation engine I382. Accordingly, for each of the confidence adjustment types, 1', above, there is a corresponding system setable parameter, “a|pha_f”, that is tunable by the adaptation engine I382. Accordingly, the following high level program segment illustrates the aggregate confidence adjustment value computed by the Analytical lleasoner Controller. target_MS_loc_hyps < get all currently active location hypotheses, ll, identifying the present target; for each currently active location hypothesis, hyp(i), from target_MS_loc_liyps do { for each of the confidence adjustment subrnodules, CA. do activate CA with hyp(i) as input; /" now compute the aggregate confidence adjustment using the output from the confidence adjustment submodules. */ aggregate_adjustment(0 < -~ alpha_path_match *' path__matcli (i) + alpha__velocity " vel_oli(i) + alpha _path_similar "' path_similar(i) + alpha_velocity_similar * velocity__similar(i) + alpha_acceleration_similar"’ acceleration_similar(i) + alpha_extrapolation *‘ extrapolation_chk(i); hyp(i).confidence < hyp(i).confidence + aggiegate_adjustment(i); } Historical Location lieasoner The historical location reasoner module I424 may be, for example, a daemon or expert system rule base. The module adjusts the confidences of currently active location hypotheses by using (from location signature data base i320) historical signal data correlated with: (a) verified MS locations (e.g. locations verified when emergency personnel co-locate with a target MS location), and (b) various environmental factors to evaluate how consistent the location signature cluster for an input location hypothesis agrees with such historical signal data. This reasoner will increase/decrease the confidence of a currently active location hypothesis depending on how well its associated loc sigs correlate with the loc sigs obtained from data in the location signature data base. 99 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCTIU S97! 15892 Note that the embodiment hereinbelow is but one of many embodiments that may adjust the confidence of currently active location hypotheses appropriately. Accordingly. it is important to note other embodiments of the historical location reasoner functionality are within the scope of the present invention as one skilled in the art will appreciate upon examining the techniques utilized within this specification. For example, calculations of a confidence adjustment factor may be determined using Monte Carlo techniques as in the context adjuster I326. Each such embodiment generates a measurement of at least one of the similarity and the discrepancy between the signal characteristics of the verified location signature clusters in the location signature data base and the location signature cluster for an input currently active location hypothesis, “loc_hyp”. The embodiment hereinbelow provides one example of the functionality that can be provided by the historical location reasoner I424 (either by activating the following programs as a daemon or by transforming various program segments into the consequents of expert system rules). The present embodiment generates such a confidence adjustment by the following steps: (a) comparing. for each cell in a mesh covering of the most relevant MS location estimate in “loc_hyp”, the location signature cluster of the "loc_hyp" with the verified location signature clusters in the cell so that the following are computed: (i) a discrepancy or error measurement is determined, and (ii) a corresponding measurement indicating a likelihood or confidence of the discrepancy measurement being relatively accurate in comparison to other such error measurements; (b) computing an aggregate measurement of both the errors and the confidences determined in (a); and (c) using the computed aggregate measurement of (b) to adjust the confidence of “loc_hyp". The program illustrated in APPENDIX E provides a more detailed embodiment of the steps immediately above. location Extrapolator The location extrapofator 1432 works on the following premise: if fora currently active location hypothesis there is sulficient previous related information regarding estintates of the target MS (e.g, from the same TOM or from using a “most likely" previous target MS estimate output by the location engine I39), then an extrapolation may be perfonned for predicting future target MS locations that can be compared with new location hypotheses provided to the blackboard. Note that interpolation routines (e.g., conventional algorithms such as Lagrange or Newton polynomials) may be used to determine an equation that approximates a target MS path corresponding to a currently active location hypothesis Subsequently, such an extrapolation equation may be used to compute a future target MS location. for further infonnation regarding such interpolation schemes, the following reference is incorporated herein by reference: Mathews, I992, Numerical methods for mathematics, science, and engineering. Englewood Cliffs, N J: Prentice Hall Accordingly, if a new currently active location hypothesis (c.g., supplied by the context adjuster) is received by the blackboard, then the target MS location estimate ofthe new location hypothesis may be compared with the predicted location. Consequently, a confidence CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 adjustment value can be determined according to how well if the location hypothesis "l" . That is. this confidence adjustment value will be largeras the new MS estimate and the predicted estimate become closertogether. Note that in one embodiment of the present invention, such predictions are based solely on previous target MS location estimates output by location engine 89. Thus. in such an embodiment, substantially every currently active location hypothesis can be provided with a confidence adjustment value by this module once a sufficient number of previous target MS location estimates have been output Accordingly, a value, extrapolation_chk(i). that represents how accurately the new currently active location hypothesis (identified here by “i") matches the predicted location is determined. Hypothesis Generating Module The hypothesis generating module I423 is used for generating additional location hypotheses according to, forexample, MS location infomiation not adequately utilized or modeled. Note, loation hypotheses may also be decomposed here if. for example it is determined that a location hypothesis includes an MS area estimate that has subareas with radically different characteristics such as an area that includes an uninhabited area and a densely populated area. Additionally, the hypothesis generating module I 428 may generate “poor reception" location hypotheses that specify MS location areas of known poor reception that are “near” or intersect currently active location hypotheses. Note, that these poor reception location hypotheses may be specially tagged (e.g., with a distinctive FOM__lD value or specific tag field) so that regardless of substantially any other location hypothesis conlidence value overlapping such a poor reception area. such an area will maintain a confidence value of "unknown" (i.e., zero). Note that substantially the only exception to this constraint is location hypotheses generated from mobile base stations I48. 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PC'l‘IUS97I15892 Mobile Base Station Location Subsystem Description Mobile Base Station Subsystem Introduction Any collection of mobile electronics (denoted mobile location unit) that is able to both estimate a location of a target MS I40 and communicate with the base station network may be utilited by the present invention to more accurately locate the target MS. Such mobile location units may provide greater target MS location accuracy by, for example, homing in on the target MS and by transmitting additional MS location information to the location center l42. lhere are a number of embodiments for such a mobile location unit contemplated by the present invention. For example, in a minimal version, such the electronics of the mobile location unit may be little more than an onboard MS MD, a sectored/directional antenna and a controller for communicating between them. Thus, the onboard MS is used to communicate with the location center l42 and possibly the target MS I40, while the antenna monitors signals for homing in on the target MS l40. in an enhanced version of the mobile location unit, a GPS receiver may also be incorporated so that the location of the mobile location unit may be determined and consequently an estimate of the location of the target MS may also be determined. However, such a mobile location unit is unlikely to be able to determine substantially more than a direction of the target MS I40 via the sectored/directional antenna without further base station infrastmcture cooperation in, for example. determining the transmission power level of the target MS or varying this power level. Thus, if the target MS or the mobile location unit leaves the coverage area I20 or resides in a poor communication area. it may be difficult to accurately determine where the target MS is located. None-the-less. such mobile location units may be sufficient for many situations. and in fact the present invention contemplates their use. However, in cases where direct communication with the target MS is desired without constant contact with the base station infrastructure, the present invention includes a mobile location unit that is also a scaled down version of a base station I22. Thus, given that such a mobile base station or MBS I48 includes at least an onboard MS I40, a sectored/directional antenna, a GPS receiver, a scaled down base station I22 and sufficient components (including a controller) for integrating the capabilities of these devices, an enhanced autonomous MS mobile location system can be provided that can be effectively used in, for example, emergency vehicles, air planes and boats. Accordingly, the description that follows below describes an embodiment of an MBS I48 having the above mentioned components and capabilities for use in a vehicle. As a consequence of the HHS I48 being mobile, there are fundamental differences in the operation of an MBS in comparison to other types of BS’: I22 (I52). In particular, othertypes of base stations have fixed locations that are precisely determined and known by the location center, whereas a location of an MES I48 may be known only approximately and thus may require repeated and frequent re-estimating. Secondly, other types of base stations have substantially fixed and stable communication with the location center (via possibly other BS's in the case of LBSs I52) and therefore although these BS‘: may be more reliable in their in their ability to communicate information related to the location of a target MS with the location center. accuracy can be problematic in poor reception areas. Thus, llBS's may be used in areas (such as wilderness areas) where there may be no other means for reliably and cost effectively locating a target MS I40 (i.e., there may be insufficient fixed location BS's coverage in an area). I02 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 Fig. II provides a high level block diagram architecture of one embodiment of the MBS location subsystem I508. , Accordingly, an MBS may include components for communicating with the fixed location BS network infrastructure and the location center I42 via an on-board transceiver l5l2 that is effectively an MS I40 integrated into the location subsystem I508. Thus, if the MBS I48 travels through an area having poor infrastructure signal coverage, then the MBS may not be able to communicate reliably with the location center I42 (e.g., in rural or mountainous areas having reduced wireless telephony coverage). So it is desirable that the NBS I48 must be capable of functioning substantially autonomously from the location center. In one embodiment, this implies that each IIBS I48 must be capable of estimating both its own location as well as the location of a target MS I40. Additionally, many commercial wireless telephony technologies require all BS's in a network to be very accurately time synchronized both for transmitting MS voice communication as well as for other services such as MS location. Accordingly, the NBS I48 will also require such time synchronitation. However, since an MBS I48 may not be in constant communication with the fixed location BS network (and indeed maybe off-line for substantial periods of time), on-board highly accurate timing device may be necessary. In one embodiment, such a device may be a commercially available ribidium oscillator I520 as shown in Fig. ll. Since the IIBS I48 , includes a scaled down version of a BS I22 (denoted I522 in fig. ll), it is capable of performing most typical BS I22 tasks, albeit on a reduced scale. In particular, the base station portion of the M85 I48 can: (a) raise/lower its pilot channel signal strength, (b) be in a state of soft hand-off with an MS I40, and/or (c) be the primary BS I22 for an MS I40, and consequently be in voice communication with the target MS (via the M85 operator telephony interface I524) if the MS supports voice communication. Further, the MBS I48 can, if it becomes the primary base station communicating with the MS I40. request the MS to raise/lower its power or, more generally, control the communication with the MS (via the base station components I522). However, since the NBS I48 will likely have substantially reduced telephony traffic capacity in comparison to a standard infrastructure base station I22, note that the pilot channel for the MBS is preferably a nonstandard pilot channel in that it should not be identified as a conventional telephony traffic bearing BS I22 by llS’s seeking normal telephony communication. Thus, a target MS I40 requesting to be located may, depending on its capabilities. either automatically configure itself to scan for certain predetermined IIBS pilot channels, or be instructed via the fixed location base station network (equivalently BS infrastructure) to scan for a certain predetermined MBS pilot channel. Moreover, the IIBS I48 has an additional advantage in that it can substantially increase the reliability of communication with a target MS I40 in comparison to the base station infrastructure by being able to move toward or track the target MS I40 even if this MS is in (or moves into) a reduced infrastructure base station network coverage area. furthermore, an MBS I48 may preferably use a directional or smart antenna I526 to more accurately locate a direction of signals from a target MS I40. Thus, the sweeping of such a smart antenna I526 (physically or electronically) provides directional information regarding signals received from the target MS I40. That is, such directional information is determined by the signal propagation delay of signals from the target MS I40 to the angular sectors of one of more directional antennas I526 on-board the MBS I48. 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 Before proceeding to further details of the NBS location subsystem I508, an example of the operation of an NBS I48 in the context of responding to a 9ll emergency call is given. In particular, this example describes the high level computational states through which the IIBS I40 transitions, these states also being illustrated in the state transition diagram of Fig. I2. Note that this figure illustrates the primary state transitions between these NBS I48 states. wherein the solid state transitions are indicative of a typical “ideal” progression when locating or tracking a target NS I40. and the dashed state transitions are the primary state reversions due, for example, to difficulties in locating the target NS I40. Accordingly, initially the NBS I48 may be in an inactive state I700, wherein the NBS location subsystem I508 is effectively available for voice or data communication with the fixed location base station network, but the NS I40 locating capabilities of the NBS are not active. from the inactive state I700 the NBS (e.g., a police or rescue vehicle) may enter an active state I704 once an NBS operator has logged onto the NBS location subsystem of the NBS, such logging being for authentication, verification and joumaling of NBS I48 events. In the active state I704, the NBS may be listed by a 9ll emergency centerand/or the location center I42 as eligible for service in responding to a 9lI request. from this state, the NBS I48 may transition to a ready state I708 signifying that the NBS is ready for use in locating and/or intercepting a target NS I40. That is. the NBS I48 may transition to the ready state I708 by performing the following steps: (la) Synchronizing the timing of the location subsystem I500 with that of the base station network infrastructure. In one embodiment, when requesting such time synchronization from the base station infrastructure, the NBS I48 will be at a predetermined or well known location so that the NBS time synchronization may adjust for a known amount of signal propagation delay in the synchronization signal. (lb) Establishing the location of the NBS I48. In one embodiment, this may be accomplished by, for example, an NBS operator identifying the predetermined or well known location at which the NBS I48 is located. (lc) Communicating with, for example, the 9lI emergency center via the fixed location base station infrastructure to identify the NBS I48 as in the ready state. Thus, while in the ready state I708, as the NBS I48 moves, it has its location repeatedly (re)-estimated via, for example, GPS signals, location center l42S location estimates from the base stations I22 (and I52), and an on-board deadreckoning subsystem I527 having an NBS location estimator according to the programs described hereinbelow. However, note that the accuracy of the base station time synchronization (via the ribidium oscillator I520) and the accuracy of the NBS I48 location may need to both be periodically recalibiated according to (la) and (lb) above. Assuming a 9ll signal is transmitted by a target NS I40, this signal is transmitted, via the fixed location base station infrastructure, to the 9H emergency center and the location center I42. and assuming the NBS I48 is in the ready state I708, if a corresponding 9ll emergency request is transmitted to the NBS (via the base station infrastructure) from the 9H emergency center or the location center, then the NBS may transition to a seek state l7|2 by performing the following steps: (2a) Communicating with, for example, the 9H emergency response centervia the fixed location base station network to receive the PN code Iorthe target NS to be located (wherein this communication is performed using the NS-like transceiver lS|2 and/or the NBS operator telephony interface I524). I04 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 (lb) Obtaining a most recent target NS'location estimate from either the 9f I emergency center or the location center I42. (Zc) inputting by the NBS operator an acknowledgment of the target NS to be located. and transmitting this acknowledgment to the 9ll emergency response center via the transceiver |Sl2. Subsequently, when the NBS I48 is in the seek state l7l2, the NBS may commence toward the target NS location estimate provided. Note that it is likely that the NBS is not initially in direct signal contact with the target NS. Accordingly, in the seek state l7l2 the following steps may be, for example, performed: (33) The location center I42 or the 9f I emergency response center may inform the target NS, via the fixed location base station network, to lower its threshold for soft hand-off and at least periodically boost its location signal strength. Additionally. the target NS may be informed to scan for the pilot channel of the NBS I48. (Note the actions here are not, actions performed by the NBS MB in the “seek state”; however, these actions are given here for clarity and completeness.) (Sb) Repeatedly, as sufficient new NS location information is available. the location center I42 provides new NS location estimates to the NBS I48 via the fixed location base station network (3c) The NBS repeatedly provides the NBS operator with new target NS location estimates provided substantially by the location center via the fixed location base station network. (3d) The NBS I48 repeatedly attempts to detect a signal from the target NS using the Pll code for the target NS. (3e) The NBS I48 repeatedly estimates its own location (as in other states as well), and receives NBS location estimates from the location center. Assuming that the NBS l48 and target MS MO detect one another (which typically occurs when the two units are within .25 to 3 miles of one another), the NBS enters a contact state l7l6 when the target NS I40 enters a soft hand-off state with the NBS. Accordingly, in the contact state l7l6, the following steps are. for example, performed: (4a) The NBS l48 repeatedly estimates its own location. (4b) Repeatedly, the location center l42 provides new target NS l40 and NBS location estimates to the NBS I48 via the fixed location base infrastructure network. (4c) Since the NBS M8 is at least in soft hand-off with the target NS I40, the NBS can estimate the direction and distance of the target NS itself using, for example, detected target NS signal strength and TOA as well as using any recent location center target NS location estimates. (4d) The NBS l48 repeatedly provides the NBS operator with new target NS location estimates provided using NS location estimates provided by the NBS itself and by the location center via the fixed location base station network. When the target NS I40 detects that the NBS pilot channel is sufficiently strong, the target NS may switch to using the NBS I48 as its primary base station. When this occurs, the NBS enters a control state I720, wherein the following steps are, for example, performed: 10 20 25 30 CA 02265875 1999-03-09 wo 98/10307 PCT/US97I15892 (5a) The NBS I48 repeatedly estimates its own location. (Sb) Repeatedly, the location center I42 provides new target NS and NBS location estimates to the NBS I48 via the network of base stations I22 (I52). (Sc) The NBS I48 estimates the direction and distance of the target NS I40 itself using, for example, detected target NS signal strength and TOA as well as using any recent location center target NS location estimates. (Sd) The NBS I48 repeatedly provides the NBS operator with new target NS location estimates provided using MS location estimates provided by the NBS itself and by the location center I42 via the fixed location base station network. (Se) The NBS I48 becomes the primary base station for the target NS I40 and therefore controls at least the signal strength output by the target NS. Note, there can be more than one NBS I48 tracking or locating an NS I40. There can also be more than one target NS I40 to be tracked concurrently and each target NS being tracked may be stationary or moving. NBS Subsystem Architecture An NBS I48 uses NS signal characteristic data for locating the NS I40. The NBS I48 may use such signal characteristic data to facilitate determining whether a given signal from the NS is a “direct shot" or an multipath signal. That is. in one embodiment. the NBS I48 attempts to determine or detect whether an MS signal transmission is received directly, or whether the transmission has been reflected or deflected. for example. the NBS may determine whether the expected signal strength, and TOA agree in distance estimates for the NS signal transmissions. Note. other signal characteristics may also be used, if there are sufficient electronics and processing available to the NBS I48; i.e., determining signal phase and/or polarity as other indications of receiving a “direct shot” from an NS I40. In one embodiment, the NBS I48 (fig. ll) includes an NBS controller I533 lor controlling the location capabilities of the NBS I48. In particular, the NBS controller I533 initiates and controls the NBS state changes as described in Fig. I2 above. Additionally, the NBS controller I533 also communicates with the location controller I535. wherein this latter controller controls NBS activities related to NBS location and target NS location; e.g., this performs the program, “mobile_base_station_controller" described in APPENDIX A hereinbelow. The location controller I535 receives data input from an event generator I537 for generating event records to be provided to the location controller I535. For example, records may be generated Irom data input received from: (a) the vehicle movement detector I539 indicating that the NBS I48 has moved at least a predetermined amount and/or has changed direction by at least a predetermined angle, or (b) the NBS signal processing subsystem l54I indicating that the additional signal measurement data has been received from either the location center I42 or the target NS I40. Note that the NBS signal processing subsystem l54I , in one embodiment, is similar to the signal processing subsystem I220 of the location center I42. may have multiple command schedulers. In particular. a scheduler I528 lor commands related to communicating with the location center I42, a scheduler I530 for commands related to GPS communication (via GPS receiver l53I), a scheduler I529 for commands related to the frequency and granularity of the reporting of NBS changes in direction and/or position via the NBS dead reckoning subsystem I527 I06 10 15 20 25 30 CA 02265875 1999-03-09 wo 98/10307 PCT/US97/15892 (note that this scheduler is potentially optional and that such commands may be provided directly to the deadreckoning estimator I544). and a scheduler I532 for communicating with the target NS(s) I40 being located. Further, it is assumed that there is sufficient hardware and/or software to appear to perform commands in different schedulers substantially concurrently. ln order to display an NBS computed location of a target NS I40, a location of the NBS must be known or determined. Accordingly, each NBS I48 has a plurality of NBS location estimators (or hereinafter also simply referred to as location estimators) for determining the location of the NBS. Each such location estimator computes NBS location information such as NBS location estimates, changes to NBS location estimates, or, an NBS location estinrator may be an interface for buffering and/or translating a previously computed NBS location estimate into an appropriate fonnat. In particular, the NBS location module l536, which determines the location of the NBS, may include the following NBS location estimators l540 (also denoted baseline location estimators): (a) a GPS location estimator l540a (not individually shown) for computing an NBS location estimate using GPS signals, (b) a location center location estimator l540b (not individually shown) for buffering and/or translating an NBS estimate received from the location center M2, (c) an NBS operator location estimator l540c (not individually shown) for buffering and/or translating manual NBS location entries received from an NBS location operator, and (d) in some NBS embodiments, an LBS location estimator l540d (not individually shown) for the activating and deactivating of l.BS's I52. Note that, in high multipath areas and/or stationary base station marginal coverage areas, such low cost location base stations l52 (LBS) may be provided whose locations are fixed and accurately predetermined and whose signals are substantially only receivable within a relatively small range (e.g., 2000 feet), the range potentially being variable. Thus, by communicating with the l.BS's I52 directly, the NBS I48 may be able to quickly use the location information relating to the location base stations for determining its location by using signal characteristics obtained from the lBSs I52. Note that each of the NBS baseline location estimators lS40, such as those above, provide an actual NBS location rather than, for example, a change in an NBS location. further note that it is an aspect of the present invention that additional NBS baseline location estimators I540 may be easily integrated into the NBS location subsystem I508 as such baseline location estimators become available. for example, a baseline location estimator that receives NBS location estimates from reflective codes provided, for example, on streets or street signs can be straightforwardly incorporated into the NBS location subsystem lS08. Additionally, note that a plurality of NBS location technologies and their corresponding NBS location estimators are utilized due to the fact that there is currently no single location technology available that is both sufficiently fast, accurate and accessible in substantially all terrains to meet the location needs of an NBS I48. For example, in many terrains GPS technologies may be sufficiently accurate; however, GPS technologies: (a) may require a relatively long time to provide an initial location estimate (e.g., greater than 2 minutes); (b) when GPS communication is disturbed, it may require an equally long time to provide a new location estimate; (c) clouds, buildings and/or mountains can prevent location estimates from being obtained; (d) in some cases signal reflections can substantially skew a location estimate. As another example, an NBS I48 may be able to use triangulation or I07 10 15 20 25 30 CA 02265875 1999-03-09 wo 98/10307 PCT/US97/15892 trilateralilation technologies to obtain a location estimate; however, this assumes that there is sufficient (fixed location) infrastructure BS coverage in the area the NBS is located. further, it is well known that the multipath phenomenon can substantially distort such location estimates. Thus, for an NBS I48 to be highly effective in varied terrains, an NBS is provided with a plurality of location technologies, each supplying an NBS location estimate. In fact, much of the architecture of the location engine I39 could be incorporated into an NBS I48. for example, in some embodiments of the NBS M0, the following f0IIs I224 may have similar location models incorporated into the NBS: (a) a variation of the distance TON I224 wherein TOA signals from communicating fixed location BS’s are received (via the NBS transceiver l5l2) by the NBS and used for providing a location estimate; (b) a variation of the artificial neural net based f0Ns I224 (or more generally a location learning or a classification model) maybe used to provide NBS location estimates via, for example. learned associations between fixed location BS signal characteristics and geographic locations; (c) an l.BS location FON I224 for providing an NBS with the ability to activate and deactivate I.BS’s to provide (positive) NBS location estimates as well as negative NBS location regions (i.e., regions where the NBS is unlikely to be since one or more l.BS’s are not detected by the NBS transceiver); (d) one or more NBS location reasoning agents and/or a location estimate heuristic agents for resolving NBS location estimate conflicts and providing greater NBS location estimate accuracy. for example. modules similar to the analytical reasoner module l4|6 and the historical location reasoner module I424. However, for those NBS location models requiring communication with the base station infrastmcture, an alternative embodiment is to rely on the location center I42 to perform the computations for at least some of these NBS FON models. That is, since each of the NBS location models mentioned immediately above require communication with the network of fixed location BS’s I22 (I52), it may be advantageous to transmit NBS location estimating data to the location center I42 as if the NBS were another NS I40 for the location center to locate, and thereby rely on the location estimation capabilities at the location center rather than duplicate such models in the NBS I48. The advantages of this approach are that: (a) an NBS is likely to be able to use less expensive processing powerand software than that of the location center: (b) an NBS is likely to require substantially less memory, particularly for data bases, than that of the location center. As will be discussed further below, in one embodiment of the NBS I48, there are confidence values assigned to the locations output by the various location estimators I540. Thus, the confidence for a manual entry of location data by an NBS operator may be rated the highest and followed by the confidence for (any) GPS location data, followed by the confidence for (any) location center location I42 estimates, followed by the confidence for (any) location estimates using signal characteristic data from LBSs. However, such prioritization may vary depending on, for instance, the radio coverage area I20. In an one embodiment of the present invention, it is an aspect of the present invention that for NBS location data received from the GPS and location center, their confidences may vary according to the area in which the NBS I48 resides. That is, if it is known that for a given area, there is a reasonable probability that a GPS signal may suffer multipath distortions and that the location center has in the past provided reliable location estimates, then the confidences for these two location sources may be reversed. I08 10 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCTIUS97/15892 In one embodiment of the present invention, NBS operators may be requested to occasionally manually enter the location of the NBS I48 when the NBS is stationary for determining and/or calibrating the accuracy of various NBS location estimators. There is an additional important source of location information for the NBS l4B that is incorporated into an NBS vehicle (such as a police vehicle) that has no comparable functionality in the network of fixed location BS's. That is, the NBS MS may use deadreckoning information provided by a deadreckoning NBS location estimator I544 whereby the NBS may obtain NBS deadreckoning location change estimates. Accordingly, the deadreckoning NBS location estimator I544 may use, for example, an on- board gyroscope l550, a wheel rotation measurement device (e.g., odometer) l554, and optionally an accelerometer (not shown). Thus, such a deadreckoning NBS location estimator l544 periodically provides at least NBS distance and directional data related to NBS movements from a most recent NBS location estimate. Nore precisely, in the absence of any other new NBS location information, the deadreckoning NBS location estimator I544 outputs a series of measurements. wherein each such measurement is an estimated change (or delta) in the position of the NBS [48 between a request input timestamp and a closest time prior to the timestamp, wherein a previous deadreckoning terminated. Thus, each deadreckoning location change estimate includes the following llEldS2 (a) an “earliest timestamp" field for designating the start time when the deadreckoning location change estimate commences measuring a change in the location of the NBS; (b) a “latest timestamp" field for designating the end time when the deadreckoning location change estimate stops measuring a change in the location of the NBS; and (c) an NBS location change vector. That is, the “latest timestamp" is the timestamp input with a request for deadreckoning location data, and the “earliest timestamp" is the timestamp of the closest time. T, prior to the latest timestamp, wherein a previous deadreckoning output has its a timestamp at a time equal to T. further, the frequency of such measurements provided by the deadreckoning subsystem I527 may be adaptively provided depending on the velocity of the NBS l4B and/or the elapsed time since the most recent NBS location update. Accordingly, the architecture of at least some embodiments of the NBS location subsystem l50B must be such that it can utilize such deadreckoning information for estimating the location of the NBS I48. ln one embodiment of the NBS location subsystem I508 described in further detail hereinbelow, the outputs from the deadreckoning NBS location estimator I544 are used to synchronize NBS location estimates from different NBS baseline location estimators. That is, since such a deadreckoning output may be requested for substantially any time from the dead reckoning NBS location estimator. such an output can be requested for substantially the same point in time as the occurrence of the signals from which a new NBS baseline location estimate is derived. Accordingly, such a deadreckoning output can be used to update other NBS location estimates not using the new NBS baseline location estimate. it is assumed that the error with dead reckoning increases with deadreckoning distance. Accordingly, it is an aspect of the embodiment of the NBS location subsystem I508 that when incrementally updating the location of the NBS l4B using deadreckoning and applying deadreckoning location change estimates to a “most likely area" in which the NBS I48 is believed to be, this area is l09 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCTIUS97/ 15892 incrementally enlarged as well as shifted. The enlargement of the area is used to account for the inaccuracy in the deadreckoning capability. Note, however, that the deadreckoning NBS location estimator is periodically reset so that the error accumulation in its outputs can be decreased. ln particular, such resetting occurs when there is a high probability that the location of the NBS is known. For example, the deadreckoning NBS location estimator may be reset when an NBS operator manually enters an NBS location or verifies an NBS location, or a computed NBS location has sufficiently high confidence. fhus, due to the NBS I48 having less accurate location information (both about itself and a target NS M0), and furtherthat deadreckoning information must be utilized in maintaining NBS location estimates, a first embodiment of the NBS location subsystem architecture is somewhat different from the location engine I39 architecture. That is, the architecture of this first embodiment is simpler than that of the architecture of the location engine l39. However, it important to note that. at a high level, the architecture of the location engine I39 may also be applied for providing a second embodiment of the NBS location subsystem l50B, as one skilled in the art will appreciate after reflecting on the architectures and pmcessing provided at an NBS I48. For example, an NBS location subsystem lSOB architecture may be provided that has one or more first order models I224 whose output is supplied to. for example, a blackboard or expert system for resolving NBS location estimate conflicts, such an architecture being analogous to one embodiment of the location engine I39 architecture. furthermore, it is also an important aspect of the present invention that, at a high level. the NBS location subsystem architecture may also be applied as an alternative architecture for the location engine I39. For example, in one embodiment of the location engine l39, each of the first order models l224 may provide its NS location hypothesis outputs to a corresponding “location track," analogous to the NBS location tracks described hereinbelow, and subsequently, a most likely NS current location estimate may be developed in a "current location track” (also described hereinbelow) using the most recent location estimates in other location tracks. further, note that the ideas and methods discussed here relating to NBS location estimators I540 and NBS location tracks, and, the related programs hereinbelow are sufficiently general so that these ideas and methods may be applied in a number of contexts related to determining the location of a device capable of movement and wherein the location of the device must be maintained in real time. for example, the present ideas and methods may be used by a robot in a very cluttered environment (e.g., a warehouse), wherein the robot has access: (a) to a plurality of “robot location estimators" that may provide the robot with sporadic location information, and (b) to a deadreckoning location estimator. Each NBS l48, additionally, has a location display (denoted the NBS operator visual user interface I558 in fig. I I) where area maps that may be displayed together with location data. In particular. NS location data may be displayed on this display as a nested collection of areas, each smaller nested area being the most likely area within (any) encompassing area for locating a target NS I40. Note that the NBS controller algorithm below may be adapted to receive location center I42 data for displaying the locations of other NBSs I48 as well as target NSs l40. further, the NBS l4B may constrain any location estimates to streets on a street map using the NBS location snap to street module I562. for example, an estimated NBS location not on a street may be “snapped to" a nearest street location. Note that a nearest street location determiner may use “ normal" orientations of vehicles on streets as a constraint on the nearest street location. ll0 10 15 20 25 30 CA 02265875 1999-03-09 W0 93,,o3o7 PCT/US97ll5892 Particularly, il an NBS I48 is moving at typical rates of speed and acceleration, and without abrupt changes direction. For example. if the deadreckoning NBS location estimator I544 indicates that the NBS I48 is moving in a northerly direction, then the street snapped to should be a north-south running street. Moreover, the NBS location snap to street module I562 may also be used to enhance target NS location estimates when, for example, it is known or suspected that the target NS I40 is in a vehicle and the vehicle is moving at typical rates of speed. Furthermore, the snap to street location module I562 may also be used in enhancing the location of a target N5 I40 by either the NBS I48 or by the location engine I39. In particular, the location estimator I344 or an additional module between the location estimator I344 and the output gateway I356 may utilize an embodiment of the snap to street location module I562 to enhance the accuracy ol target NS I40 location estimates that are known to be in vehicles. Note that this may be especially useful in locating stolen vehicles that have embedded wireless location transceivers (NSs I40), wherein appropriate wireless signal measurements can be provided to the location center I42. NBS Data Structure Remarks Assuming the existence ol at least some of the location estimators I540 that were mentioned above, the discussion here refers substantially to the data structures and their organization as illustrated in Fig. I3. lhe location estimates (or hypotheses) lor an NBS I48 determining its own location each have an error or range estimate associated with the NBS location estimate. That is, each such NBS location estimate includes a "most likely NBS point location" within a "most likely area". The “most likely NBS point location" is assumed herein to be the centroid ol the "most likely area." In one embodiment of the NBS location subsystem I500, a nested series of “most likely areas" maybe provided about a most likely NBS point location. However, to simplify the discussion herein each NBS location estimate is assumed to have a single "most likely area". One skilled in the art will understand how to provide such nested “most likely areas” Irom the description herein. Additionally, it is assumed that such “most likely areas" are not grossly oblong; i.e., area cross sectioning lines through the centroid of the area do not have large dillerences in their lengths. for example, for any such "most likely area", A, no two such cross sectioning lines of A may have lengths that vary by more than a lactor ol two. Each NBS location estimate also has a confidence associated therewith providing a measurement of the perceived accuracy of the NBS being in the "most likely area" of the location estimate. A (NBS) “location track” is an data structure (or object) having a queue ol a predetermined length for maintaining a temporal (timestamp) ordering ol “location track entries” such as the location track entries l770a, l770b, l774a, l774b, l77Ba. I778b. |7B2a, I7B2b, and l7B6a (Fig. I3), wherein each such NBS location track entry is an estimate of the location of the NBS at a particular corresponding time. There is an NBS location track for storing NBS location entries obtained from NBS location estimation inlormation from each ol the NBS baseline location estimators described above (i.e., a GPS location track I750 for storing NBS location estimations obtained from the GPS location estimator I540. a location center location track I754 [or storing NBS location estimations obtained Irom the location estimator I540 deriving its NBS location estimates Irom the location center I42, an LBS location track I758 lor storing NBS 10 15 20 25 30 CA 02265875 1999-03-09 WO 98110307 PCT /US97/ 15892 location estimations obtained from the location estimator I540 deriving its NBS location estimates from base stations I22 and/or I52, and a manual location track I762 for NBS operator entered NBS locations). Additionally, there is one further location track, denoted the “current location track" I766 whose location track entries may be derived fmm the entries in the other location tracks (described further hereinbelow). further, for each location track, there is a location track head that is the head of the queue for the location track. The location track head is the most recent (and presumably the most accurate) NBS location estimate residing in the location track. Thus, for the GPS location track I750 has location track head I770; the location center location track l7S4 has location track head I774; the [BS location track I758 has location track head I778; the manual location track l762 has location track head I782; and the current location traclt I766 has location track head I786. Additionally, for notational convenience, for each location track, the time series of previous NBS location estimations (i.e., location track entries) in the location track will herein be denoted the “path for the location track." Such paths are typically the length of the location track queue containing the path. Note that the length of each such queue may be determined using at least the following considerations: (i) In certain circumstances (described hereinbelow), the location track entries are removed from the head of the location track queues so that location adjustments may be made. in such a case. it may be advantageous for the length of such queues to be greater than the number of entries that are expected to be removed: (ii) In determining an NBS location estimate, it may be desirable in some embodiments to provide new location estimates based on paths associated with previous NBS location estimates provided in the corresponding location track queue. Also note that it is within the scope of the present invention that the location track queue lengths may be a length of one. llegarding location track entries, each location traclt entry includes: (a) a “derived location estimate" for the NBS that is derived using at least one of: (i) at least a most recent previous output from an NBS baseline location estimator i540 (i.e., the output being an NBS location estimate); (ii) deadreckoning output information from the deadreckoning subsystem I527. further note that each output from an NBS location estimator has a "type" field that is used for identifying the NBS location estimator of the output. (b) an “earliest timestamp" providing the time/date when the earliest NBS location information upon which the derived location estimate for the NBS depends. Note this will typically be the timestamp of the earliest NBS location estimate (from an NBS baseline location estimator) that supplied NBS location information used in deriving the derived location estimate for the N85 I48. (c) a “latest timestamp" providing the time/date when the latest NBS location information upon which the derived location estimate for the NBS depends. Note that earliest timestamp = latest timestamp only for so called “baseline entries" as defined hereinbelow. Further note that this attribute is the one used for maintaining the “temporal (timestamp) ordering" of location track entries. H2 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT/U S97/ 15892 (d) A “deadreckoning distance" indicating the total distance (e.g., wheel turns or odometer difference) since the most recently previous baseline entry lor the corresponding NBS location estimator for the location track to which the location track entry is assigned. For each NBS location track, there are two categories of NBS location tratlc entries that may be inserted into a NBS location track: (a) “baseline" entries. wherein each such baseline entry includes (depending on the location track) a location estimate lot the NBS l48 derived from: (i) a most recent previous output either lrom a corresponding NBS baseline location estimator, or (ii) mm the baseline entries of other location tracks (this latter case being the for the "current" location track); (b) “extrapolation" entries, wherein each such entry includes an NBS location estimate that has been extrapolated from the (most recent) location track head lorthe location track (i.e., based on the track head whose “latest timestamp" immediately precedes the latest timestamp of the extrapolation entry). Each such extrapolation entry is computed by using data lrom a related deadreckoning location change estimate output from the deadreckoning NBS location estimator lS44. Each such deadreckoning location change estimate includes measurements related to changes or deltas in the location of the NBS I48. More precisely, loreach location track, each extrapolation entry is determined using: (i) a baseline entry, and (ii) a set of one or more (i.e., all later occurring) deadreckoning location change estimates in increasing “latest timestamp" order. Note that for notational convenience this set of one or more deadreckoning location change estimates will be denoted the “deadreckoning location change estimate set" associated with the extrapolation entry resulting from this set. (c) Note that for each location track head, it is eithera baseline entry or an extrapolation entry. further. for each extrapolation entry, there is a most recent baseline entry, B, that is earlier than the extrapolation entry and it is this B from which the extrapolation entry was extrapolated. This earlier baseline entry, 8, is hereinalter denoted the "baseline entry associated with the extrapolation entry." Note generally, for each location traclc entry, T, there is a most recent previous baseline entry. B, associated with I, wherein il T is an extrapolation entry, then B is as defined above. else if T is a baseline entry itsell. then l= B. Accordingly, note that for each extrapolation entry that is the head of a location track, there is a most recent baseline entry associated with the extrapolation entry. Further, there are two categories of location tracks: (a) “baseline location tracks,” each having baseline entries exclusively from a single predetermined NBS baseline location estimator: and (b) a “current” NBS location traclc having entries that are computed or determined as “most likely" NBS location estimates lrom entries in the other NBS location tracks. l|3 10 20 . W0 98/ 10307 CA 02265875 1999-03-09 PCT /US97/ 15892 NBS location Estimating Strategy In order to be able to properly compare the track heads to determine the most likely NBS location estimate it is an aspect of the present invention that the track heads of all location tracks include NBS location estimates that are for substantially the same (latest) timestamp. However, the NBS location information from each NBS baseline location estimator is inherently substantially unpredictable and unsynchronized. In fact. the only NBS location information that may be considered predicable and controllable is the deadreckoning location change estimates from the dead reckoning NBS location estimator I544 in that these estimates may reliably be obtained whenever there is a query from the location controller I535 for the most recent estimate in the change of the location for the NBS I48. Consequently (referring to fig. l3), synchronization records I190 (having at least a |790b portion, and in some cases also having a l790a portion) may be provided for updating each location track with a new NBS location estimate as a new track head. ln particular, each synchronization record includes a deadreckoning location change estimate to be used in updating all but at most one of the location track heads with a new NBS location estimate by using a deadreckoning location change estimate in conjunction with each NBS location estimate from an NBS baseline location estimator, the location track heads may be synchronized according to timestamp. More precisely, for each NBS location estimate. E. from an NBS baseline location estimator. the present invention also substantially simultaneously queries the deadreckoning NBS location estimator for a corresponding most recent change in the location of the NBS I48. Accordingly, E and the retrieved NBS deadreckoning location change estimate. C, have substantially the same “latest timestamp“. Thus, the location estimate E may be used to create a new baseline track head for the location track having the corresponding type for E, and C may be used to create a corresponding extrapolation entry as the head of each of the other location tracks. Accordingly, since for each NBS location estimate. E, there is a NBS deadreckoning location change estimate, C, having substantially the same “latest timestamp“. E and C will be hereinafter referred as "paired." High level descriptions of an embodiment of the location functions performed by an NBS I48 are provided in APPENDIX A hereinbelow. ll4 10 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/US97/ 15892 APPENDIX A: MBS Function Embodiments Mobile Base Station Controller Program mobile_base__station__controller() { wait_ /or_ /'/rpr/r_ a/_ I/'rrr_NBJ'_ /oar/'0/l(event); /* “event” is a record (object) with MBS location data */ WHILE (no MBS operator input to exit) D0 CASE OF (event): /"’ determine the type of "event" and process it. */ MR5 LOCATION DATA RECEIVED FROM OPS‘: MD! LOCATION DA TA RECEIVED FROM LBS: MB5 LOCATION DATA RECEIVED FROM ANY OTHER IIIGIIL Y RELIABLE M85 LOCATION SOURCES (EXCEPT LOCATION CENTER): MBS_new_est < get__ new_ I/B.l'_ /ocat/b/r__ usi/rg_ est/'mate(eve nt); /* llote, whenever a new MBS location estimate is entered as a baseline estimate into the location tracks, the other location tracks must be immediately updated with any deadreckoning location change estimates so that all location tracks are substantially updated at the same time. ‘I dead reclt_est < get; deadreckon/'/rg_ /ocar/o/r_ r/Ia/rge_ err/'mare(event); l‘lBS_curr_est < DETERMlNE_MBS_LOCATl0N_EST|MATE(llBS_new_est, deadreck_est); if (llBS_curr_est.conlidence > a predetermined high confidence threshold) then re.ret_ deadreckon/'/rg_ /‘/BJ'_ /ocar/o/)_ estimate/(event); /* deadreckoning starts over from here. */ /"' Send MBS location information to the Location Center. "/ if ( MBS has not moved since the last MBS location estimate of this type and is not now moving) then { configure the MBS on-board transceiver (e.g., MBS-MS) to immediately transmit location signals to the fixed location BS network as if the MBS were an ordinary location device (MS); communicate with the Location Center via the fixed location BS infrastructure the following: (a) a “locate me" signal. ll5 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/US97I 15892 (b) NBS__curr_est. (c) NBS_new_est and (d) the timestamp for the present event. Additionally. any location signal information between the NBS and the present target NS maybe transmitted to the Location Center so that this information may also be used by the Location Center to provide better estimates of where the NBS is. further, if the NBS determines that it is immediately adjacent to the target NS and also that its own location estimate is highly reliable (e.g., a GPS estimate). then the N85 may also communicate this information to the Location Center so that the Location Center can: (a) associate any target NS location signature cluster data with the fixed base station infrastructure with the location provided by the NBS, and (b) insert this associated data into the location signature data base of the Location Center as a verified cluster of “random loc sigs”; /* note, this transmission preferably continues (i.e., repeats) for at least a predetermined length of time of sufficient length for the Signal Processing Subsystem to collect a sulficient signal characteristic sample size. '/ } else SCHEDULE an event (if none scheduled) to transmit to the Location Center the following: (a) NBS_curr_est, and (b) the GPS location of the NBS and the time of the GPS location estimate; I’ llow update NBS display with new NBS location; note. NBS operator must request NBS locations on the NBS display; if not requested. then the following call does not do an update. */ 1/pdate_ /‘/B.f__ ope/ator__ drlrp/ay_ w/t/r_ I/B.f_ en(NBS_curr_est); } SINCE LAST M53 LOCATION UPDATE M0! HAS MOVED A THRESHOLD DISTANCE { dead reck_est < gt-(_ o’eao’re'cA'a/r/irg_ /oazIion__ c/1ange_ em'rrrate(event); /* Obtain from NBS Dead Reckoning Location Estimator a new dead reckoning NBS location estimate having an estimate as to the NBS location change from the location of the last NBS location provided to the NBS. */ NBS_curr_est < DETERMlNE_MBS_LOCATl0N_ESTlMATE(NUl.l.. deadreck_est); /* this new NBS estimate will be used in new target NS estimates*/ r/pr/are_ /'/BJ'_ disp/ay_ wit/z_ updateo'_ /‘/B5; /amt/b/r(NBS_curr_est); SCHEDULE an event (if none scheduled) to request new GPS location data for NBS; llb 20 25 30 CA 02265875 1999-03-09 W0 98/10307 SCHEDULE an event (if none scheduled) to request communication with Location Center (LC) related to new NBS location data; PCTIUS97/15892 SCHEDULE an event (if none scheduled) to request new LBS location communication between the NBS and any LBS’s that can detect the NBS; /’ Note, in some embodiments the processing of NBS location data from LBS’: may be performed automatically by the Location Center, wherein the Location Center uses signal characteristic data from the LBS's in determining an estimated location of the NBS. ‘/ SCHEDULE an event (if none scheduled) to obtain new target NS signal characteristics from MS; /"‘ i.e.. may get a bettertarget NS location estimate now. */ } TIMER HAS EXPIRED SINCE LAST RELIABLE TARGET M5 LOCA77ON INFORMATION OBTAINED: { SCHEDULE an event (if none scheduled) to request location communication with the target NS, the event is at a very high priority; RESEI timer for target NS location communication; /* iry to get target NS location communication again within a predetermined time. Note, timer may dynamically determined according to the perceived velocity of the target NS. */ } LOCATION COMMUNICATION FROM TARGET M5 RECEIVED: { NS_raw_signal_data < ger_ /I3_'_ s4fgna[_ c/Ia/2cten1rri(__ raw_ r/ata(event); /* Note. “NS_raw_signal_data" is an object having substantially the unfiltered signal characteristic values for communications between the NBS and the target MS as well as timestamp information. "/ Construct a message for sending to the Location Center, wherein the message includes at least “NS_raw_signal_data" and “NBS_curr__est" so that the Location Center can also compute an estimated location for the target NS; SCHEDULE an event (if none scheduled) to request communication with Location Center (LC) for sending the constructed message; /* llote. this data does not overwrite any previous data waiting to be sent to the LC. */ NS_signal_data < get__ /‘/J'_ sg/r2[_ c/12/aaenit/'c_ d2ta(event); /" Note, the NS signal data obtained above is, in one embodiment. "raw" signal data. However, in a second embodiment, this data is filtered substantially as in the Location Center by the Signal ll7 CA 02265875 1999-03-09 W0 98/ 10307 PCTIUS97/1 5892 Processing Subsystem. for simplicity ol discussion here, it is assumed that each MBS includes at least a scaled down version of the Signal Processing Subsystem (see FIG. ll). */ MS_new_est < DETERMINI.-'_ M3'_ M05T_ RECEN1'_ ESTIMA TETM BS_curr_est, MS_curr_est, MS_signal_data); 5 /' May use lonvard and reverse TOA. TDOA. signal power, signal strength, and signal quality indicators. Note, “MS_curr__est” includes a timestamp of when the target MS signals were received. '/ il (MS_new__est.confidence > min__MS_conlidence ) then { 10 marl; fIS_ esr_ a.r_ re/nporary(MS_new_est); /" Note, it is assumed that this MS location estimate is “temporary" in the sense that it will be replaced by a corresponding MS location estimate received from the location Center that is based on the same target MS raw signal data. That is, if the location Center responds with a corresponding target MS location estimate, E, while “MS_new_est" is a value in a “moving window" of target MS 15 location estimates (as described hereinbelow). then E will replace the value of “MS_new_est". Note, the moving window may dynamically vary in site according to, lor example, a perceived velocity of the target MS and/or the MBS. */ MS_moving_window < get_ /‘IS_ mow’/2g__ w/ndaw(event); /"’ get moving window of location estimates for this target MS. */ 20 at/d_ /‘/L err/mare-_ ro_ )‘IS_ /ocar/'0/r_ m‘/)riaw(MS_new_est, MS_moving_window); I" Since any given single collection of measurements related to locating the target MS may be potentially misleading, a "moving window" of location estimates are used to lorm a “composite location estimate” of the target MS. This composite location estimate is based on some number of the 25 most recent location estimates determined. Such a composite location estimate may be, for example, analogous to a moving average or some other weighting of target MS location estimates. Thus, lor example, for each location estimate (i.e., at least one MS location area, a most likely single location, and, a conlidence estimate) a centroid type calculation may be performed to provide the composite location estimate.*/ 30 MS__curr__est < Diff/W//[_ /'/I__ 1001f/0:1/_ E.l'f//‘I/lf£(MS_moving_window); /“ DETERMINE new target MS location estimate. Note this may an average location or a weighted average location. ‘*/ re/nove_ Jr/lea’://eo’_ eve/1rs("TAllGET_MS_SCHEDULE”,event.MS_|D); IIB 20 25 30 CA 02265875 1999-03-09 PCT/US97/15892 /' REMOVE ANY OTHER EVENTS SCHEDULED FOR REQUESTTNG LOCATION COMMUNICATION FROM TARGET MS ‘I W0 98/ 10307 } else /* target MS location data received but it is not deemed to be reliable (e.g., too much multipath and/or inconsistent measurements, so SCHEDULE an event (if none scheduled) to request new location communication with the target MS, the event is at a high priority*/ adr/_to_sc/redo/eoj_everm(“TAllGET_l‘|S_SCllEDULE".event.MS_lD); update_ /1B.l'_ operarar_ disp/ay_ wit/r_ IIJ'_ e:r(MS_curr_est); I’ The MBS display may use various colors to represent nested location areas overlayed on an area map wherein, lor example,3 nested areas may be displayed on the map overlay: (a) a largest area having a relatively high probability that the target MS is in the area (e.g., > 95%); (b) a smaller nested area having a lower probability that the target MS is in this area (e.g., > 80%); and (c) a smallest area having the lowest probability that the target MS is in this area (e.g., >10%). Further. a relatively precise specific location is pmvided in the smallest area as the most likely single location of the target MS. Note that in one embodiment, the colon for each region may dynamically change to provide an indication as to how high their reliability is; e.g., no colored areas shown for reliabilities below, say, 40%; 40-50% is purple; 50-60% is blue; 60-70% is green; 70-80% is amber; 80-90% is white; and red denotes the most likely single location of the target NS. Further note the three nested areas may collapse into one or two as the NBS gets closer to the target NS. Moreover. note that the collapsing ol these different areas may provide operators in the NBS with additional visual reassurance that the location of the target MS is being determined with better accuracy.“’/ /"’ Now RESET timer for target MS location communication to try to get target MS location communication again within a predetermined time. */ re.rer_ ti/Ire/(“TARGET_NS_SCHEDULE”. event.l1S_lD); } COMMUNICATION 0}’ LOCATION DATA T 0 M55‘ FROM LOCATION CENTER: { /' Note, target MS location data may be received from the Location Center in the seek state, contact state and the control state. Such data may be received in response to the HHS sending target MS location signal data to the Location Center (as may be the case in the contact and control states), or such data may be received from the Location Center regardless of any previously received target MS location sent by the l‘lBS (as may be the case in the seek, contact and control states). */ 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCTIU S97/ 15892 if ( (the timestamp of the latest MBS location data sent to the Location Center) < = (the timestamp returned by this location Center communication identifying the NBS location data used by the Location Center for generating the MBS location data of the present event} ) then /* use the LC location data since it is more recent than what is currently being used. "'/ { MBS_new_est <--- ge-t__ locatio/r_ (e/rrer_I‘IB.f_ erI(event); deadreclc__est <--- get__ deadrecko/ri/rg_ /ocar/'on_ c/Iange_ em’/nate(event); MBS__curr_est < ---—DEI’ERMINE_MBS_LOCA'l'l0N_ESTlMATE(l18S_new__est, deadreck__est); if(l1BS_curr_est.confidence > a predetermined high confidence threshold) then re:er_r/eac/re(koning_/‘/B):_ /acarriz/)_est/'/rr2roI(event); r/pdate_ /‘lB5_ ope/ator_ disp/ay_ w/t/I__ )‘IB.l'_ e.r!(MBS_curr_est); } if ( (the timestamp of the latest target f’lS location data sent to the Location Center) < = (the timestamp returned by this Location Center communication identifying the MS location data used by the Location Center for generating the target MS location estimate of the present event)) then /'* use the MS location estimate from the LC since it is more recent than what is currently being used. */ { MS_new_est < ger_ laaario/i_ (erIter__ /‘/.[_ e.tt(event); /‘ This information includes error or reliability estimates that may be used in subsequent attempts to determine an NBS location estimate when there is no communication with the LC and no exact (GPS) location can be obtained. That is, if the reliability of the target MS's location is deemed highly reliable, then subsequent less reliable location estimates should be used only to the degree that more highly reliable estimates become less relevant due to the MBS moving to other locations. */ f1S_moving_window < gag //.f_ /nevi/rg_ w/'ndow(event); /" get moving window of location estimates for this target MS. */ if ( (the Location Center target MS estimate utilized the MS location signature data supplied by the M83) then if (a corresponding target MS location estimate marked as “temporary” is still in the moving window) then /* It is assumed that this new target MS location data is still timely (note the target MS may be moving); so replace the temporary estimate with the Location Center estimate. */ replace the temporary target MS /acat/onestimate in the moving window with “M$_new_est"; I20 20 25 30 CA 02265875 1999-03-09 wo 98/10307 PCT/US97/15892 else I‘ there is no corresponding "temporary" target MS location in the moving window; so this MS estimate must be too old; so don't use it. */ else I‘ the Location Center did not use the MS location data from the MBS even though the timestamp ol the latest MS location data sent to the Location Center is older that the MS location data used by the Location Center to generate the present target MS location estimate. Use the new MS location data anyway. Note there isn't a corresponding “temporary" target MS location in the moving window. ‘I add_ II!_ est/'/nare_ ra_ fI.[_ /ocarrin/r_ iv/'/2da»(llS_new_est); } else /* the MS location estimate from the LC is not more recent than the latest MS location data sent to the LC from the MBS. */ if (a corresponding target MS location estimate marked as "temporary" is still in the moving window) then /*'* It is assumed that this new target MS location data is still timely (note the target MS may be moving); so replace the temporary estimate with the Location Center estimate. "I replace the temporary target MS /oar/bnestimate in the moving window with “l‘lS_new_est”; else /* there is no corresponding "temporary" target MS location in the moving window; so this HS estimate must be too old; so don't use it */ MS_curr_est < Dfffllll//I[_/‘IL1004710//_[J7//‘I;lTt'(MS_moving_window); z/pdare_ /'IB5_ o,oeratar__ d/Zrp/a/_ wit/r_ I‘IJ’_ e.rt(l‘lS_curr_est); re.ret_ r/'meI(“LC_COMl1UlllCATl0N”, event.llS_lD); } NO COMMUNICATION FROM LC: { /* i.e., too long a time has elapsed since last communication lrom LC. */ SCHEDULE an event (if none scheduled) to request location data (MBS and/or target MS) lrom the Location Center, the event is at a high priority; re:et_r1rner(“LC_C0llMUlllCATl0ll", event.l‘lS_lD); } REQUEST T O NO LONGER CONTINUE LOCATING TIIE PRESENT TARGET ME { il (event not from operator) then request MBS operator verification; else { REMOVE the current target MS lrom the list ol llSs currently being located and/or tracked; SCHEDULE an event (if none scheduled) to send communication to the Location Center that the current target MS is no longer being tracked; lll 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/U S97! 15892 PURGE MBS of all data related to current target MS except any exact location data lor the target MS that has not been sent to the Location Center for archival purposes; } REQUEST FROM LOCATION CENTER TO ADD ANOTHER TARGET MS T 0 THE LIST OF MS: BEING TRACIIED: { /* assuming the Location Center sends MBS location data lor a new target MS to locate and/or track (e.g., at least a new MS ID and an initial MS location estimate), add this new target MS to the list ol llSs to track. Note the MBS will typically be or transitioning to in the seek state.*‘/ il (event not from operator) then request NBS operator verification; else { |llll|llLlZE MBS with data received from the Location Center related to the estimated location of the new target MS; /* e.g., initialize a new moving window lor this new target MS; initialize MBS operator interface by graphically indicating where the new target MS is estimated to be. "I CONFIGU RE MBS to respond to any signals received from the new target MS by requesting location data from the new target NS; llllTlALlZE timer for communication from LC; /* A timer may be set per target MS on list. ‘I } } REOUES T T 0 MANUALLY ENTER A LOCATION ESTIMATE FOR MBS (FROM AN MBS OPERA TOR): { /* llote, MBS could be moving or stationary. If stationary, then the estimate lor the location of the MBS is given high reliability and a small range (e.g.. 20 feet). ll the NBS is moving, then the estimate for the location of the NBS is given high reliability but a wider range that may be dependent on the speed of the MBS. In both cases, if the M85 operator indicates a low confidence in the estimate, then the range is widened, or the operator can manually enter a range."/ MS_new_est <--- ger__rrew_/‘IB!_/acario/r_es;/rom_ape/ator(event; /* The estimate may be obtained. for example, using a light pen on a displayed map "I if (operator supplies a confidence indication lor the input NBS location estimate) then llBS_new_est.conlidence < ger_ /1B.[_ ope/aror_ co/r/7r/e/rce_ a/_ em‘/rrate(event); else MBS_new_est.confidence <--- I; /* This is the highest value lora conlidence.*/ deadreck_est <--- ger_ dear/rerko/ri/rg_ /orario/r_ c/Ia/rge__ est/'/rratt=(event); l22 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT /US97/15892 NBS_curr__est < DETERMINE_MBS_l.0CA'l'l0N_ESTlMATE(NBS_new_est, deadreck_est ); il (NBS_curr_est.confdence > a predetermined high confidence threshold) then /e.rer__ dear/reolrorr/'/IL//BI_ /0(3t/0/l_ e.rtimator(event); I/pdate_ /‘/BJ'_ operaror_d/Irp/ay_ W/I/I__ /‘/B.f_ e.rt(NBS__curr__est); /* Note, one reason an NBS operator might provide a manual NBS input is that the N85 might be too inaccurate in its location. Moreover, such inaccuracies in the NBS location estimates can cause the target NS to be estimated inaccurately, since target NS signal characteristic values may be utilized by the NBS to estimate the location of the target NS as an ollset from where the NBS is. lhus, ii there are target NS estimates in the moving window of target NS location estimates that are relatively close to the location represented by “NBS__curr__est", then these select lew NS location estimates may be updated to reflect a more accurate NBS location estimate. */ NS__moving_window < get_ I/J: mow’/rg_ wi/ro’o»(event); if (NBS has not moved much since the receipt of some previous target NS location that is still being used to location the target NS) then { UPDATE those target NS location estimates in the moving window according to the new NBS location estimate here; NS_curr_est < Diff/l/‘I///{_ I‘/J; [0017/0//__ £.f/'//‘I/lftlllS_moving_window); update; I/5J'_ operator_ d/Lrp/a/_ wit/r__ //!__ e:t(NS_curr__est); } } } /' end case statement "/ Lower Level M65 Function Descriptions /' PROCEDURE: DEl’ERMlNE_MBS_LOCATlON_EST I MATE REMARKS: It is assumed that with increasing continuous dead reckoning without additional NBS location verification, the potential error in the NBS location increases. It is assumed that each NBS location estimate includes: (a) a most likely area estimate surrounding a central location and (b) a confidence value of the NBS being in the location estimate. l23 10 I5 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/US97/1 5892 The confidence value lor each NBS location estimate is a measurement ol the likelihood of the NBS location estimate being correct. More precisely, a confidence value for a new MBS location estimate is a measurement that is adjusted according to the following criteria: (a) the confidence value increases with the perceived accuracy of the new MBS location estimate (independent of any current MBS location estimate used by the MBS), (b) the confidence value decreases as the location discrepancy with the current NBS location increases, (c) the confidence value for the current NBS location increases when the new location estimate is contained in the current location estimate, (d) the confidence value for the current NBS location decrease when the new location estimate is not contained in the current location estimate, and Therelore, the confidence value is an MBS location likelihood measurement which takes into account the history of previous MBS location estimates. It is assumed that with each NBS location estimate supplied by the location Center there is a default confidence value supplied which the M85 may change. */ DETERMINE_MB$_LOCATION_ESTlMATE(MBS__new_est, deadreck_est) /’ Add the pair, “MBS_new_est” and “deadreck__est" to the location tracks and determine a new current MBS location estimate. Input: MB$_new_est A new MBS baseline location estimate to use in determining the location of the MBS, but not a (deadreckoning) location change estimate deadreck___est The deadreckoning location change estimate paired with “MBS_new__est". */ { il (llBS_new_est is not NULL) then /* the “deadreclt_est" is paired with “l‘1BS_new_est" '/ { if (all NBS location tracks are empty) then { insert “l1BS_new_est" as the head of the location track of type, “l’lBS_new_est.type"; insert “l‘lBS_new_est” as the head ol the current track; /* so now there is a “l1BS_curr_est" MBS location estimate to use "7 MBS_curr_est <--- ger_ tr//r_ e.rr(MBS_new_est.MS_|D); /* lrom current location track "’/ } I24 10 15 20 25 30 CA 02265875 1999-03-09 “'0 93’ 10307 PCT/US97/l5892 else I" there is at least one non-empty location track in addition to the current location track being non- emntv"'/ if (t1BS_new__est is of type l1ANUAL_ENTRY) then { /"’ MBS operator entered an MBS location estimate for the M85; so must use it */ llBS_curr__est < add__location_entry(MBS_new_est. deadreck_est); } else /* “MBS_new_est” is not of type MANUAL_ENTRY "I ii (the NBS location track of type, “l1BS_new_est.type”, is empty) then { I‘ some other location track is non-empty *"/ l‘lBS_curr__est < add_|ocation__entry(MBS__new_est, dcadreck_est); } else /“ “MBS__new_est.type" location track is non-empty and "MBS__new__est" is not of type MAN|.lAL_ENTRY '/ { /’ In the next statement determine if “MBS_new_est” is of at least minimal useful quality in comparison to any previous estimates of the same type; see program def’n below "/ tontinue_to_process_new_est <-- FlLTER(ll BS_new__est); ii (continue__to_process__new_est) then /* “MBS_new_est” is ol sulficient quality to continue processing. '/ MBS_curr_est < add__location_entry(l‘lBS_new_est, dead recl<_est); }/* end “MBS_new_est" not filtered out */ else /’ "MBS_new_est" is liltered out; do nothing “/; }/“‘ end else "I }/* end else at least one non-empty location track "I } else /" MBS_new_est is NULL; thus only a deadreckoning output is to be added to location tracks "’/ { extrapolation_entry < create__a/7_ext/apobt/b/r_e/71/y_/roIn(deadreck_est); /II.Ie'It_ /'/;ro_ eve/y_ /acar/'0/I_ trad(extrapolation_entry); /* including the “current location track” */ l‘lBS_curr_est <--- get_ a/rr_ e.i1(l1BS_new_est.MS_lD); /* from current location track‘ I I25 20 25 30 CA 02265875 1999-03-09 W0 98/10307 } RETURN(l1BS_curr__est); } END I“ DETERMINE_MBS_LOCAT|0N_ESTIMATE ‘/ PCT /US97Il5892 add_location_entry(MBS__new_est, deadreck_est); I‘ fhis function adds the baseline entry, “l1llS_new_est" and its paired dead reckoning location change estimate, “deadreck_est" to the location tracks. including the “current location track". Note, however, that this function will roll back and rearrange location entries, if necessary, so that the entries are in latest timestamp order. lleturns: l‘lBS_curr_est ‘I { if (there is a time series of one or more dead reckoning extrapolation entries in the location track of type “l‘fBS_new_est.type" wherein the extrapolation entries have a “latest timestamp” more recent than the timestamp of “MBS_new_est") then { I" Note, this condition may occur in a number of ways; e.g., (a) an NBS location estimate received from the Location Center could be delayed long enough (eg, l-4 sec) because of transmission and processing time; (b) the estimation records output from the NBS baseline location estimators are not guaranteed to be always presented to the location tracks in the temporal order they are created. */ roll back all (any) entries on all location tracks, including the “current" track, in “latest timestamp" descending order, until a baseline entry, 8, is at the head of a location track wherein B is a most recent entry having a “latest timestamp" prior to “llBS_new_est"; let “stack” be the stack of a location track entries rolled off the location tracks, wherein an entry in the stack is either a baseline location entry and a paired deadreclroning location change estimate, or, an unpaired deadreckoning location change estimate associated with a NULL for the baseline location entry; insert “MBS_new_est" at the head of the location track of type "MBS_new_est.type" as a new baseline entry; insert the extrapolation entry derived from “deadreck_est" in each of the other baseline location tracks except the current track; I‘ it is important to note that “deadreck_est” includes the values for the change in the NBS location substantially for the time period between the timestamp, I, of “MS_new_est" and the timestamp of the closest deadreckoning output just before I. Further note that if there are any extrapolation entries that were rolled back above, then there isan extrapolation entry, E, previously in the location tracks and wherein E has an earliest timestamp equal to the latest timestamp of 8 above. Thus, all the previous extrapolation entries removed can be put back if E is modified as follows: the MES location change vector of E (denoted herein as E.delta) becomes E.delta - [location change vector ol “deadreck_est"]. '*/ l1BS_curr_est < UPDATE__CURR_EST(l’lBS_new_est, deadreck_est); l26 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT /US97/ 15892 if (the extrapolation entry E exists) then /* i.e., “stack” is not empty ‘I { modify the extrapolation entry E as per the comment above; /" now fix things up by putting all the rolled off location entries back. including the “current location track” "I do until “stack” is empty { staclt__tnp < pop_ stad(staclt); /* “staclt_top" is either a baseline location entry and a paired deadrecltoning location change estimate, or, an unpaired deadrecltoning location change estimate associated with a NULL for the baseline location entry */ MBS_nxt__est <.-- [EL base//'rre_ £'Il(I)(St8(l<_t0p); deadreclt_est < get_ deadreckoni/rg_ em/y(staclt_top); MBS_curr_est <--- DETERMINE_MB5_LOCATl0N_ESTlMATE(llBS_nxt_est. deadreck_est); } } } else /“ there is no deadreckoning extrapolation entries in the location track of type “MBS_new__est.type" wherein the extrapolation entries have a “latest timestamp" more recent than the timestamp ol “l1BS_new_est". So just insert “M8S_new__est" and “dearlreclt_est”.*/ insert "llBS_new_est" at the head of the location track of type “MBS_new_est.type" as a new baseline entry; insert the extrapolation entry derived lrom "deadreck_est" in each of the other location tracks except the current track; llBS_curr_est < UPDATE_CURR_ES'I'(l‘lBS_new_est, deadreck_est); I‘ see prog del'n below */ } RETUllll(MBS_curr_est); } /' end add__|ocation_entry */ F|LTER(MBS_new_est) /' This function determines whether "MBS__new_est” is of sufficient quality to insert into it's corresponding MBS location track. It is assumed that the location track of “MBS__new__est.type" is non-empty. 10 I5 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCT /US97I15892 Input: MBS_new_est A new MBS location estimate to use in determining the location of the M35. Returns: FALSE if “MBS__new_est” was processed here (i.e., filtered out), TRUE if processing with “MBS_new__est" may be continued . */ { continue_to_process_new_est <--lllllE; /* assume “NBS_new_est" will be good enough to use as an NBS location estimate "/ /' see if “MBS__new_est” can be filtered out."*/ if (the confidence in NBS_new_est < a predetermined function of the conlidence(s) of previous NBS location estimates of type “NBS_new_est.type”) I‘ e.g., the predetermined lunction here could be any of a number ol functions that provide a minimum threshold on what constitutes an acceptable confidence value for continued processing of “NBS_new_est". The following is an example of one such predetermined function: ll*(conlidence of “NBS_new_est.type" location track head) for some K, 0 <l(< = L0, wherein K varies with a relative frequency of estimates of type “NBS_new_est.type" not filtered; e.g.., for a given window of previous NBS location estimates of this type, K= (number ol NBS location estimates of “NBS_new_est.type" not filtered)/(the total number of estimates of this type in the window). Note, such filtering here may be important for known areas where. for example, GPS signals may be potentially reflected from an object (i.e., multipath), or, the location Center provides an NBS location estimate of very low confidence. For simplicity, the embodiment here discards any filtered location estimates. However, in an alternative embodiment, any such discarded location estimates may be stored separately so that, for example. if no additional better NBS location estimates are received, then the filtered or discarded location estimates may be reexamined for possible use in providing a better subsequent NBS location estimate."/ then continue_to_process__new_est < -- FALSE; else if (an area for “NBS_new_est" > a predetermined function of the corresponding area(s) ol entries in the location track of type “NBS_new_est.type") I‘ e.g.. the predetermined iunction here could be any ol a number of functions that provide a maximum threshold on what constitutes an acceptable area site for continued processing of “NBS_new_est". The following are examples of such predetermined functions: (a) the identity function on the area of the head of the location track of type “NBS_new__est.type"; or, (b) ll‘ (the area of the head of the location track ol type “NBS_new__est.type"), for some K, K> = l.0, wherein for a given window ol previous NBS location estimates of this type, K= (the total number of estimates in the window)/ (number of these location estimates not filtered); note, each extrapolation entry increases the area of the head; so areas of entries at the head of each location track type grow in area as extrapolation entries are applied. *’/ then continue_to_process_new_est < -- FALSE; I28 10 15 20 25 30 CA 02265875 1999-03-09 W0 93/10307 PCT/US97/15892 RE|’URN(continue__to_process_new_est) } UPDATE_CURR_EST(MB$__new_est, deadreck__est) /' This function updates the head of the “current” MBS location track whenever “MB$_new_est" is perceived as being a more accurate estimate of the location of the M35. input: MBS_new_est A new MBS location estimate to use in determining the location of the MBS deadreclc_est The deadreclconing MBS location change estimate paired with “MBS_new__est”. Returns a potentially updated “MBS__curr_est” "/ if (llBS_new_est is of type l‘|ANUAl_ENTRY) then { /* MBS operator entered an MBS location estimate for the MBS; so must use it */ insert “l1BS_new_est" as the head of the “current MES location track" which is the location track indicating the best current approximation of the location oi the NBS; } else /* “llBS__new_est" is not a manual entry "/ { l1BS__curr_est <---gegwrge:t(MBS_new_est.l1$_lD); /* get the head of the “current location track” */ adjusted_curr_est < app/)'_ deadreckan/'/7g_ ro(l‘lBS_curr_est, deadreck_est); J‘ The above lunction returns an object of the same type as “MBS_curr_est", but with the most likely MBS point and area locations adjusted by “deadreck_est". Accordingly, this function perlorms the lollowing computations: (a) selects. Am, the M85 location area estimate of “llBS_curr_est" (e.g., one of the “most likely” nested area(s) provided by “MBS__curr_est” in one embodiment of the present invention); (b) applies the deadreckoning translation corresponding to "deadreck_est" to Ann, to thereby translate it (and expand it to at least account for deadreckoning inaccuracies). */ if (reasanabb;cIase(MBS__new_est, adjusted_curr__est, MBS_curr_est)) I‘ In one embodiment, the function “reasonably_close" here determines whether a most likely NBS point location (i.e., centroid) of "llBS_new__est” is contained in the NBS estimated area of "adjusted_curr_est" I29 U: 10 20 25 30 W0 98/ 10307 then CA 02265875 1999-03-09 PCT/US97ll5892 Note that the reasoning for this constraint is that if “NBS__curr_est" wasaccurate. then any “most likely NBS point location" of a new NBS baseline estimate that is also accurate ought to be in the NBS estimated area of “ad]usted__curr_est"‘ In a second embodiment, the function “reasonably_close" determines whether the centroid (or most likely NBS point location) of “NBS_new_est" is close enough to “NBS_curr_est" so that no MBS movement constraints are (grossly) violated between the most likely point locations of “MB$_new__est" and “MBS_curr_est"; i.e.,cunstraints on (de)acceleration. abruptness of direction change, velocity change, max velocity for the terrain. Note, such constraints are discussed in more detail in the section herein describing the “Analytical lieasoner". Accordingly, it is an aspect of the present invention to provide similar capabilities to that of the Analytical lleasoner as part of the NBS, and in particular, as the functionality of the “NBS LOCATION CONSTRAINT CHECKER" illustrated in fig. ll. lt is assumed hereinafterthat the embodiment of the function, “reasonab|y_close". performed here is a combination of both the first and second embodiments, wherein the constraints of both the first and second embodiments must be satisfied for the function to return TRUE. *"/ if (the confidence in MBS__new_est > = the confidence in MBS_curr_est) then if (the most likely NBS area of NBS_new_est contains the most likely NBS area of "adjusted_curr__est" as computed above) then shrink NBS_new_est uniformly about its centroid (i.e., “most likely NBS point location") until it is as small as possible and still contain the NBS estimated area of “adiusted_curr_est". insert_ into__ location_ track(“current", N BS_new_est); /* The program invoked here inserts a location track entry corresponding to the second parameter into the location track identified by the first parameter (e.g., “current"). lt is imponant to note that the second parameter for this program may be eir/lerof the following data structures: a “location track entry", or an “NBS location estimate" and the appropriate location track entry or entries will be put on the location track corresponding to the first parameter. ihe insertion is performed so that a “latest timestamp" order is maintained; i.e.. (a) any extrapolation entries in the location track. wherein these entries have a more recent “latest timestamp" than the ("earliest" or only) timestamp (depending on the data structure) of the second parameter are removed, and 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/ 15892 (b) conceptually at least, the location change estimates output from the deadreckoning NBS location estimator that correspond with the removed extrapolation entries are then reapplied in timestamp order to the head of the target location traclc */ } else l'* the centroid of “NBS_new_est”. is contained in an area of “NBS__curr_est", but the confidence in “MBS_new__est” < confidence in “MBS__curr__est" "I most_|ikely__est < -- determine a “most likely NBS location estimate" using the set S = {the NBS location estimate centroid(s) of any NBS location track heads contained in the NBS estimated area of “adiusted__curr_est", plus, the centroid of “NBS_new_est"}; /‘ Note. in the above statement. the “most likely NBS location estimate” may be determined using a number ol dillerent techniques depending on what lunction(s) is used to embody the meaning ol “most likely". In one embodiment, such a “most likely" function is a function ol the confidence values of a predetermined population of measurements (e.g., the selected location track heads in this case) lrorn which a "most likely” measurement is determined (e.g., computed or selected). for example, in one embodiment, a “most likely" function may include selecting a measurement having the maximum confidence value from among the population of measurements. in a second embodiment, a "most likely" function may include a weighting of measurements (e.g., location track heads) according to corresponding confidence values of the measurements. for example, in the present context (of NBS location track heads) the following steps provide an embodiment ol a “most likely" lunction: (a) determine a centroid of area for each of the selected track heads (i.e., the location track heads having a point location estimate contained in the NBS estimated area of “adjusted_curr_est"); (b) determine the “most likely location NBS position" P as a weighted centroid of the centroids from step (a), wherein the weighting ol each of the centroids from (a) is provided by their corresponding confidence values; (c) output an area. A,, as the “most likely NBS location area", wherein the centroid of A, is P and A. is the largest area within the NBS estimated area ol “adjusted_curr_est" satisfying this condition; and (d) set a co nlidence value lor A. as the average confidence value of “NBS_new_est". “NBS_curr_est" and the selected location track head used. */ insert_ /'nta__ lacatian_ tracl¢(“current". most_likely__est); l3l 10 15 20 25 30 CA 02265875 1999-03-09 W0 98,103.” PCT/US97I1S892 else /‘* “MBS_new_est” is not reasonably close to "adiusted_curr_est” (i.e., “MBS__curr_est" with “deadreck__est" applied to it), so a conflict exists here; e.g., (i) “NBS__new_est” is not a manual entry, and (ii) “NBS__new_est" does not have its centroid contained in the NBS estimated area ol “adjusted_curr_est”, or, there has been a movement constraint violation. Note that it is not advisable to just replace “NBS_curr_est" with “new_est_head" because: (a) “llBS_new_est" may be the NBS location estimate that is least accurate, while the previous entries ol the current location track have been accurate; (b) the "NBS__curr_est" may be based on a recent NBS operator manual entry which should not be overridden. "‘/ NBS_curr_est < resolve_conflicts(N BS_new_est, ad]usted_curr_est, NBS_curr_est); } } /* end else “NBS_new_est" not a manual entry “I il (NBS is a vehicle) and (not oil road) then I‘ it is assumed that a vehicular NBS is on-road unless explicitly indicated otherwise by NBS operator. */ NBS_curr_est < .map_ ta_ be.ct_ f7r_ stm-t(NBS_curr_est); I‘ snap to best street location according to location estimate. velocity, and/or direction of travel. Note, this is a translation of “NBS_curr_est". ‘I IlETURN(MBS__curr_est) } I‘ END UPDATE(MBS_Cl.lRlt_ES'|') “/ resolve__conflicts(MBS_new__est, adiusted_curr__est, MBS_curr__est) /" There is a basic conflict here, (i) “MBS_new_est" is not a manual entry, and (ii) one of the following is true: "MBS_new_est" does not have its centroid contained in the area “adiusted_curr_est", or, using “MBS__new__est” implies an M85 movement constraint violation. Input: MBS__new_est The newest NBS location estimate record. adjusted_curr_est The version of “NBS_curr_est” adiusted by the deadreckoning location change estimate paired with “NBS_new_est”. MBS_curr_est The location track entry that is the head ol the “current“ location track. Note that "NBS_new_est.conlidence" > “NBS_curr_est.cofidence". Output: An updated “MBS_curr__est". ‘ 10 15 20 25 30 W0 98/10307 CA 02265875 1999-03-09 PCT/US97/15892 mark that a conflict has arisen between “NBS_curr_est" and “NBS_new_est"; if (the NBS operator desires notification of NBS location estimate conflicts) then notify the NBS operator of an NBS location estimate conflict; if (the NBS operator has configured the NBS location system to ignore new estimates that are not “reasonably close” to adjustecl_curr_est) or (MBS_curr__est is based on a manual MBS operator location estimate,and the NBS has moved less than a predetermined distance (wheel turns) from where the manual estimate was provided) then RETURN(adiusted_curr_est); else I‘ not required to ignore “MBS__new_est”, and there has been no recent manual estimate input‘/ { /' try to use “MBS_new_est" "'/ if ((MBS_new_est.confidence - adjusted_curr_est.conlidence) > a large predetermined threshold) then /* Note, the confidence discrepancy is great enough so that “NBS_new_est" should be the most recent baseline estimate on current NBS location track. Note that the threshold here may be approximately 0.3. wherein confidences are in the range [0, l]."/ insert_into_ Iocatian_ tracl¢("current", NBS__new_est); /* insert “NBS_new__est" into “current” location track (as a baseline entry) in "latest timestamp" order; i.e., remove any extrapolation entries with a more recent “latest timestamp" in this track. and reapply, in timestamp order, the location change estimates output from the deadreckoning NBS location estimator that correspond with the removed extrapolation entries removed; "‘/ else /‘* “MBS_new__est.confidence” is not substantially bigger than "adiusted_curr_est.confidence”; so check to see if there are potentially MBS location system instabilities '*/ { /’ check for instabilities "'/ if [ (there has been more than a determined fraction of conflicts between the “NBS_curr_est" and “NBS__new_est" within a predetermined number ol most recent “NBS__new_est” instantiations) or (the path corresponding to the entries of the “current location track" of the NBS has recently violated NBS movement constraints more than a predetermined fraction of the number of times there has been new instantiation ol “NBS__curr_est", wherein such movement constraints may be (de)acce|eration constraints. abrupt change in direction constraints. constraints relating to too high a velocity for a terrain) or (there has been an NBS operator indication of lack of confidence in the recently displayed NBS location estirnates)] I33 CA 02265875 1999-03-09 W0 98/ 10307 PCT/US97/15892 then /* the MBS location system is likely unstable and/or inaccurate; check to see if this condition has been addressed in the recent past. */ { I" fix instability "I if (lix_instability_counter equal to 0) then /* no instabilities have been addressed here 5 within the recent past; i.e., “lix_instability_counter" has the following semantics: if it is 0, then no instabilities have been addressed here within the recent past: else if not 0, then a recent instability has been attempted to be fixed here. Note, “fix__instal>ility_coumer” is decremented, if not zero, each time a new baseline location entry is inserted into its corresponding baseline location track. Thus, this counter provides a "wait and see" strategy to determine if a previous 10 performance of the statements below mitigated the (any) NBS location system instability. */ most_likely_est < -- determine a new “most likely HBS location estimate"; [30.l] /* Note, a number of MS location estimates may be generated and compared here for determining the "most_llkely_est". For example, various weighted centroid MBS location 15 estimates may be determined by a clustering of location track head entries in various ways. in a first embodiment for determining a value (object) for “most_likely_est". a "most likely” function may be performed, wherein a weighting of location track heads according to their corresponding confidence values is performed. For example. the following steps provide an embodiment of a "most likely" function: 20 (a) obtain a set S having: (i) a centroid of area for each of the track heads having a corresponding area contained in a determined area surrounding the point location of “ad]usted_curr_est" (e.g., the NBS estimated area of “adiusted_curr_est"), plus (ii) the centroid of “llBS_new_est“; (b) determine the "most likely location NBS pasitiari‘ P as a weighted centroid of 25 the centroids of the set S from step (a), wherein the weighting of each of the centroids from (a) is provided by their corresponding confidence values; (c) output an area, A, as the “most likely NBS location area” wherein A has P as a centroid and A is a “small” area (e.g., a convex hull) containing the corresponding the centroids of the set S; and 30 (cl) set a conlideiice value for A as the average confidence value of the centroids of the set S. In a second embodiment, “most_lil<ely_est" may be determined by expanding (e.g., substantially uniformly in all directions) the MBS location estimate area of “ll8S_new_est" l34 10 15 20 25 30 W0 98/10307 CA 02265875 1999-03-09 PCT/US97/15892 until the resulting expanded area contains at least the most likely point location of "adjusted_curr__est” as its most likely NBS location area. "/ insert_ into_ lacation_ trar:Ic(“curre nt", most__likely_est); lix_instability_counter < a predetermined number, C, corresponding to a number of baseline entries to be put on the baseline location tracks until MBS location system instabilities are to be addressed again here; /* when this counter goes to zero and the NBS location system is unstable. then the above statements above will be performed again. Note, this counter must be reset to C (or higher) il a manual MBS estimate is entered. */ } } /“’ fix instability ‘/ else /" The MES location system has been reasonably stable, and "MBS_curr_est.confidence" is not substantially bigger than “adjusted__new__est.confidence” . */ most_|ikely_est < -- determine a most likely l‘lBS location estimate; /* ihe determination in the statement above may be similar or substantially the same as the computation discussed in relation to statement [30.l] above. However. since there is both more stability in this case than in [30.l] and less confidence in “llBS_new__est". certain MBS movement constraints may be more applicable here than in [30.l]. Accordingly, note that in any embodiment for determining “most_likely__est" here, reasonable movement constraints may also be used such as: (a) unless indicated otherwise, an NBS vehicle will be assumed to be on a road. (b) a new MBS location estimate should not imply that the M88 had to travel faster than, for example, l20 mph or change direction too abruptly or change velocity too abruptly or traverse a roadless region (e.g., corn field or river) at an inappropriate rate of speed. Thus, once a tentative NBS location estimate (e.g., such as in the steps of the first embodiment of [30.|]) lor “most_likely_est“ has been determined, such constraints may be applied to the tentative estimate for determining whether it should be pulled back toward the centroid of the “MBS_curr_est” in order to satisfy the movement constraints*/ ins'ert_into_lacatian__ tracIr(“current”, most_lilcely_est); /* note, the second parameter lor this function may be either of the following data structures: a “location track entry”, or a “MBS location estimate” and the appropriate location track entry or entries will be put on the location track corresponding to the first parameter. */ 5 CA 02265875 1999-03-09 W0 98/ 10307 } I’ check for instabilities ‘I MBS_curr_est <--- grt_ curr_ e.t2(M8S_new__est.MS_|D); /* from current location track */ } /“ try to use “MBS__new_est” "’/ REI'URN(MBS__curr__est) } /‘ END reso|ve_conflicts ‘I I36 PCT /U S97] 15892 10 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/US97l15892 APPENDIX B: Pseudo code for a genetic algorithm Pseudo code for a genetic algorithm Genetic_Algorithm ("decode, ‘litness_function, par-ms) I‘ This program implements a genetic algorithm for determining ellicient valrres ol parameters for a search pmblem. The current best values of the paranreters are received by the genetic algorithm in a data structure such as an array. ll no such infomration is available, then the genetic algorithm receives random guesses ol the parameter values. This program also receives as input a pointerto a decode lunction that provides the genetic algorithm with inlorrnation about how the parameters are represented by bit strings (see genetic algorithm relerences). The program also receives a pointerto a fitness function. “l'rtness_lunctions", that provides the genetic algorithm with inlormation about how the quality of potential solutions should be determined. The program computes new, impmved values of parameters and replaces the old values in the array "pamrs" ‘I // assume that each particular applimtion will have a specific fitness lunction and decoding // scheme; otherwise. the pmcedure is the sanre every time //generate the inialalpopulatrbn // generate a random population of binary strings containing popsize strings lor i = l to popsize lorj = I to string_|ength strirrg(r,D = random(0,l) and loop on j end loop on i // keep generating new populations until finished do umil linished lori = l to popsize //tnnsforrn the binary string: into parametersfmm the problem at hand; requires problem //specific function decode (string6)) // evaluate each string evaluate (stringG)) end loop on i // perform reproduction reproduce (population_ol_strings) CA 02265875 1999-03-09 WO 98110307 AVpem&»n1c7osmaMar crossover (popu|ation_of__string:) Afixvfinnrnuuaabn mutate (population_of_strings) 5 // evaluate the new population tori = I to popsize AVaannbnnthebwnystnhgsfiuv;uwmmeavs A7fitmnthe;u1uflbn1athand:nnnwbespnubhun Aflpeaflbfinuflbn 10 decode (string(:)) ,Vewubaaeflhafihasofeachsaiqg evaluate (stringG,j)) end loop on i if finished then report new results to the calling routine 15 else go back to tip of do-until loop B8 PCT/US97/15892 10 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/ 15892 APPENDIX C: Location Database Maintenance Programs onrn BASE PROGRAMS I=oIt MAINTAINING me Location SIGNATURE DATA BASE In the algorithms below, external parameter values needed are underlined. Note that in one embodiment of the present invention. such parameters may be adaptively tuned using, for example, a genetic algorithm. EXTERNALLY INVOCABLE PROGRAMS: Update_Loc_Sig_DB(new_loc_obj, selection_criteria, loc_sig_pop) /' This program updates loc sigs in the Location Signature data base. That is, this program updates, for example, at least the location inlonnation lot verilied random loc sigs residing in this data base. Note that the steps herein are also provided in llowchart form in fig. l7a through FIG. l7C. Introductory Information Related to the Function, “Update_Loc__Sig_DB" ihe general strategy here is to use inlormation (i.e.. “new__loc_ob]”) received from a newly verified location (that may not yet be entered into the Location Signature data base) to assist in determining ii the previously stored random verified loc sigs are still reasonably valid to use for: (29.l) estimating a location for a given collection (i.e., ‘‘bag‘') of wireless (e.g., CDMA) location related signal characteristics received lrorn an NS. (29.2) training (for example) adaptive location estimators (and location hypothesizing models), and (293) comparing with wireless signal characteristics used in generating an MS location hypothesis by one of the MS location hypothesizing models (denoted First Order Models, or, FOMs). More precisely, since it is assumed that it is more likely that the newest location inlormation obtained is more indicative of the wireless (CDHA) signal characteristics within some area surrounding a newly veriiied location than the verified loc sigs (location signatures) previously entered into the Location Signature DB. such verified loc sigs are compared for signal characteristic consistency with the newly verified location inlormation (object) input here ior determining whether some of these "older" data base verified loc sigs still appropriately characterite their associated location. ln particular, comparisons are iteratively made here between each (target) loc sig “near" “new_loc__obj" and a population of lot sigs in the location signature data base (such population typically including the loc sig lor “new_loc_obj) lor: I0 20 25 30 W0 98/ 10307 CA 02265875 1999-03-09 PCT/US97/ 15892 (29.4) adjusting a confidence factor of the target loc sig. Note that each such confidence factor is in the range [0, I] with 0 being the lowest and I being the highest. Further note that a confidence factor here can be raised as well as lowered depending on how well the target loc sig matches or is consistent with the population of lot sigs to which it is compared. Thus, the confidence in any particularverified loc sig, LS, can fluctuate with successive invocations of this program if the input to the successive invocations are with location information geographically "near" LS. (29.5) remove older verified loc sigs from use whose confidence value is below a predetermined threshold. Note, it is intended that such predetermined thresholds be substantially automatically adjustable by periodically testing various confidence factor thresholds in a specified geographic area to determine how well the eligible data base loc sigs (for different thresholds) perform in agreeing with a number of verified loc sigs in a “loc sig test-bed”, wherein the test bed may be composed of, for example, repeatable loc sigs and recent random verified loc sigs. Note that this program may be invoked with a (verified/known) random and/or repeatable loc sig as input. furthermore, the target loc sigs to be updated may be selected from a particular group of loc sigs such as the random loc sigs or the repeatable loc sigs, such selection being determined according to the input parameter, “selectio n_criteria” while the comparison population may be designated with the input parameter, “loc_sig__pop". For example, to update confidence factors of certain random loc sigs near "new_loc__ob]", “selection_criteria" may be given a value indicating, “USE_llAllD0ll_l0C_S|GS", and “|oc_sig_pop” may be given a value indicating, "USE_REPEATABlE__LOC_SlGS". Thus, if in a given geographic area, the repeatable loc sigs (from, e.g., stationary transceivers) in the area have recently been updated, then by successively providing “new_loc_obj" with a loc sig for each of these repeatable loc sigs, the stored random loc sigs can have their confidences adjusted. Alternatively, in one embodiment of the present invention, the present function may be used for determining when it is desirable to update repeatable loc sigs in a particular area (instead of automatically and periodically updating such repeatable loc sigs). Forexample, by adjusting the confidence factors on repeatable loc sigs here provides a method for determining when repeatable loc sigs for a given area should be updated. That is. for example, when the area's average confidence factor for the repeatable loc sigs drops below a given (potentially high) threshold, then the l‘lSs that provide the repeatable loc sigs can be requested to respond with new loc sigs for updating the DB. Note, however, that the approach presented in this function assumes that the repeatable location information in the D3 is maintained with high confidence by, for example, frequent DB updating. Thus, the random verified D8 location information may be effectively compared against the repeatable loc sigs in an area. INPUT: new__loc_obi: a data representation at least including a loc sig for an associated location about which Location Signature loc sigs are to have their confidences updated. I40 10 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/US97/ 15892 selection__criteria: a data representation designating the loc sigs to be selected to have their confidences updated (may be defaulted). The following groups ol loc sigs may be selected: “llSE_RAllD0l1_LOC_SlGS” (this is the default), USE_llEl’ElllABLE__LOC_SlGS", "USE_ALL__LOC_S|GS". Note that each of these selections has values for the lollowing values associated with it (although the values may be delaulted): (a) a confidence reduction factor for reducing loc sig conlidences, (b) a big error threshold for determining the errors above which are considered too big to ignore, (c) a confidence increase factor for increasing loc sig confidences. (d) a small error threshold for determining the errors below which are considered too small (i.e., good) to ignore. (e) a recent time for specilying a time period for indicating the loc sigs here considered to be “recent”. loc_sig_pop: a data representation of the type ol loc sig population to which the loc sigs to be updated are compared. The following values may be provided: (a) “USE ALL LOC SlGS lN DB”. (b) “USE ONLY llEPEAIABl£ LOC SIGS" (this is the delault), (c) “USE ONLY LDC SIGS Wllll SIMILAR TIME Of DAY” However,environmentalcharacteristics such as: weather. tralfic, season are also contemplated. */ /' Make sure “new_ lac_ obj” is in Location DB. */ ii (NOT new_loc_obj.in_DB ) then /* this location object is not in the Location Signature DB; note this can be determined by comparing the location and times/datestamp with DB entries ‘/ DB__ irm-rt_ /rew__ /oc__:/:g_ em/e.t(new_loc_obj); // stores loc sigs in Location Signature DB /' Determine a geographical area surrounding the location associated with “new_ lac_ obj” for adjusting the confidence factors of loc srgs having associated locations in this area. ’/ DB_search_areal < ger__ confide-nce_ adj:/.rt__ searc/r__ area_ /or_ 0B_ rando/n_ /oc_ sgr.i(new_loc_obj.location); /* get the loc s/gs to have their confidence factors adjusted. */ DB_|oc__sigs < ger_ all_ M_ lac_ 5/'g5_ for(DB_search_areal, selection_criteria); nearby_loc_sig_bag < get loc sigs lrom “DB_loc__sigs" wherein for each loc sig the distance between the location associated with “new_loc_obj.location" and the verified location for the loc sig is closer than. lor example. some standard deviation (such as the second standard deviation) of these distances lor all loc sigs in “DB__loc_sigs”; /' For each “loc srg” having its confidence factor adjusted do '/ for each loc_sig[i] in nearby_loc_sig_bag do // determine a confidence for these random loc sigs l4l 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 { /" Determine a search area surrounding the location associated with "lac sly" "/ lot < gr;-t_ ver/'lied__ /oration(loc_sig[i]); PCT lUS97l 15892 /' Determine the error corresponding to how well “loc sig” fits with the portion of the inputted type of loc sly population that is also in the search area. '/ BS <--- get_B5(loc_sigfi]); marl;_ar_ unacce.uable(|oc_sig[i]); I‘ mark “lot_sig[i]” in the location Signature DB so that it isn't retrieved. */ DB__search_area2 < get_ rorrfidence_ aoy'u.rt_ rem/r_2rea_lar_ 0B__Io(_:/gs(lot.lotation); /' Get searth area about “rand_loc". Typically, the “new__loc_ob]” would be in this search area */ loc_sig_bag < c/e2te__ /o(__ .r@_ bag(Ioc_sig[i]); /“ create a lot sig bag having a single lot sig. “lot__sig[fl"*/ output_criteria < get criteria to input to “Determine_Location_Signature_lit__Errors" indicating that the function should generate error records in the returned “error_rec_bag" only lor the single lot sig in “|oc_sig_bag". That is, the output criteria is: “OUTPUT E|ill0K_llECS FOR INPUT l.0C SIGS ONLY". error_ret_bag[i] < Determine__Location__Signature__Fit_Errors(loc.location, loc_sig_bag, DB_search_area2, loc_sig_pop, output_criteria); an/nark_ /nalring_arcmable(|oc_sig[i]); I‘ unmark “lot_sig[i]" in the Location Signature DB so that it can now be retrieved. */ } /" lfeduce confidence factors of loc sigs: (a) that are nearby to the location associated with "new_loc_ obj”, (b) that have big errors, and (c) that have not been recently updated/acquired. '/ error__rec_set < ma/re_.ret_ um'an_ ol(error_rec_bag[fl for all i); I‘ Now modily confidence: oi loc sigs in DB and delete loc sigs with very low confrdences ‘I reduce_bad_D B_|oc_sigs(nearby_loc_sig_bag, error_rec_set. selection_triteria.big_error__threshold, selectio n_criteria.confidence_red uctio n_lacto r, selection_criteria.recent_time); /" Increase confidence factors of lac sigs: (a) that are nearby to the location associated with "new__ loc_ obj”, (b) that have small errors, and (c) that have not been recently updated/acquired. "/ 10 15 20 25 30 CA 02265875 1999-03-09 W0 98! 10307 PCT /U S97! 15892 increase_confidence__of_good_DB_loc_sigs(nearby_loc_sig_bag. error_rec_set. selection_criteria.small_error_threshold. selection_criteria.confidence_increase_factor, selection__criteria.recent__time); END OF Update_Loc_Sig_DB DB_Loc_Sig_Error__Fit(MS__loc_est, DB_search_area, measured_loc_sig_bag, search_criteria) /" This function determines how well the collection of loc sigs in "measured_loc_sig_bag” fit with the loc sigs in the location signature data base wherein the data base loc sigs must satisly the criteria of the input parameter “search_criteria" and are relatively close to the MS location estimate of the location hypothesis, "hypothesis”. Thus. in one embodiment of the present invention, the present function maybe invoked by, lorexample, the confidence adjuster module to adjust the confidence of a location hypothesis. lnput: hypothesis: MS location hypothesis; measurecl_|oc_sig_bag: A collection of measured location signatures (“loc sigs" lor short) obtained from the M8 (the data structure here is an aggregation such as an array or list). Note, it is assumed that there is at most one loc sig here per Base Station in this collection. Additionally, note that the input data structure here may be a location signature cluster such as the “loc_sig_c|uster" field of a location hypothesis (cf. Fig. 9). Note that variations in input data structures may be accepted here by utilization of [lag or tag hits as one skilled in the art will appreciate; search_criteria: The criteria for searching the verified location signature data base for various categories of lot sigs. The only limitation on the types of categories that may be provided here is that. to be useful, each category should have meaningful number of lot sigs in the location signature data base. lhe following categories included here are illustrative. but others are contemplated: (a) “USE ALL LOC SIGS IN DB" (the default), (b) “USE ONLY REPEAIABLE LOC SIGS", (c) “USE ONLY LDC SlGS Wlfll Sllflllllt TIME OF DAY". Further categories of loc sigs close to the MS estimate of “hypothesis” contemplated are: all loc sigs for the same season and same time of day, all loc sigs during a specific weather condition (e.g., showing) and at the same time of day, as well as other limitations for other environmental conditions such as traffic patterns. Note, if this parameter is NIL. then (a) is assumed. Returns: An error object (data type: “error_object”) having: (a) an “error” field with a measurement of the error in the fit of the location signatures from the MS with verified location signatures in the Location Signature data base; and 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 (b) a “confidence” field with avalue indicating the perceived confidence that is to be given to the “error” value. */ if ("search_criteria" is NIL) then search_criteria < "USE ALL LOC SIGS lll DB"; /' determine a collection of error records wherein there is an error record for each 85 that is associated with a Ioc sig in ”measure__ loc__ srg_ bag” and for each 35' associated with a lot sig in a geographical area surrounding the hypothesis’: location. "/ output_criteria < “OUTPUT ALL POSSIBLE Ellll0lt_RECS"; /* The program invoked in the following statement is described in the location signature data base section. ‘I error_rec_bag < Determine_Location__Signature__Fit_Errors(l1S_loc_est, measure-d_loc_sig_bag, DB__search_area, search_criteria, output_criteria); /"' Note, “error_rec_bag” has “error_rec's" for each BS having a lot sig in “DB_search_area" as well as each BS having a loc sig in "measured_loc_sig_bag". '/ /" determine which error records to ignore "/ BS__errors_to_ignore_bag < get_BJ'_ error_ rm__ to_ /jg/rore(DB_search_area, error_rec_bag,); /’ Our general strategy is that with enough BSs having: (a) loc sigs with the target MS, and (b) also having verified locations within an area about the MS location “l1S_loc_est", some relatively large errors can be tolerated or ignored. For example, if the MS location estimate. “llS_loc_est", here is indeed an accurate estimate of the MS's location and if an area surrounding “llS_loc_est" has relatively homogeneous environmental characteristics and the area has an adequate number of verified location signature clusters in the location signature data base. then there will be presumably enough comparisons between the measured MS Ioc sigs of “measured_loc_sig_bag" and the estimated Ioc sigs, based on verified MS locations in the DB (as determined in "Determine_Location_Signature_fit__Errors"), for providing “error_rec_bag" with enough small errors that these small errors provide adequate evidence for “MS_|oc_est” being accurate. Accordingly, it is believed that, in most implementations of the present invention, only a relatively small number ofloc__ srg comparisons need have small errors for there to be consistency between the lac sigs of "measured__ loc_ sig_ bag" and the verified Ioc sigs in the location signature data base. That is, a few large errors are assumed, in general, to be less indicative of the M5 location hypothesis being incorrect than small errors are indicative of accurate M5 locations. lhus, if there were ten measured and estimated loc sig pairs, each associated with a different BS, then if four pairs have small errors, then that might be enough to have high confidence in I44 10 20 25 30 CA 02265875 1999-03-09 W0 93,1039-; PCT/US97/15892 the MS location hypothesis. However, note that this determination could depend on the types of base stations; e.g.., if five full-service base stations had measured and verified loc sigs that match reasonably well but live location BSs in the search area are not detected by the MS (i.e.. the measured_|oc_sig_bag has no loc sigs for these location BS5), then the confidence is lowered by the mismatches. Thus, for example, the largest x% of the errors in “error_rec_bag" may be ignored. Note, that “x" may be: (a) a system parameter that is tunable using, for example, a genetic algorithm; and (b) “x” may be tuned separately for each different set of environmental characteristics that appear most important to accurately accessing discrepancies or errors between loc sigs. Thus, for a first set of environmental characteristics corresponding to: rural, flat terrain. summer, 8 PM and clear weather, it may be the case that no loc sig errors are ignored. Whereas, for a second set of environmental characteristics corresponding to: dense urban, hilly. fall, 8 PM, heavy traffic. and snowing, all but the three smallest errors may be ignored. */ /" determine (and return) error object based on the remaining error records */ error_obj.measmt < 0; // initializations error_obj.confidence < 0; for each error_rec[i] in (error_rec_bag - BS_errors_to_ignore_bag) do { error_obj.measmt <--- error__obj.measmt + (error_rec[i].error); error_obj.conlidence <--- error_obj.confidence + (error_rec[i].conlidence); } error_obi.measmt < error_obj.measmt / SlZEOf(error_rec_bag - BS_errors__to_ignore_bag); error_ob].conlidence < error_obi.confidence /S|ZEOf(error_rec_bag - BS_errors_to_ignore__bag); RETURN(error_obj); ENDOF DB_Loc__Sig_Error_Fit INTERNAL PROGRAMS: reduce_bad_DB_Ioc_sigs(loc_sig_bag , error_rec_set, big_error_threshold confidence__reduction_factor, recent_time) /* This program reduces the confidence of verified DB loc sigs that are (seemingly) no longer accurate (i.e., in agreement with comparable loc sigs in the DB). If the confidence is reduced low enough, then such loc sigs are removed from the DB. further. if fora DB verified location entity (referencing a collection of loc sigs for the same location and time), this entity no longer references any valid loc sigs, then it is also removed from the location signature data base I320. .Note that the steps herein are also pmvided in flowchart form in figs. l8a through l8b. Inputs: 20 25 30 CA 02265875 1999-03-09 W0 93/10307 PCT /U S97l 15892 |oc__sig_bag: The lot sigs to be tested for determining if their confidences should be lowered and/or these |oc sigs removed. error_rec_set: Ihe set of "error_recs" providing information as to how much each lot sig in “loc_sig_bag" disagrees with comparable lot sigs in the DB. That is, I/It'll! in” “error rec"/rare for each /ac:/kin "lor sig bag". big_error_threshold: Ihe error threshold above which the errors are considered too big to ignore. confidence_reduction__factor-. The factor by which to reduce the confidence of |oc sigs. recent_time: lime period beyond which loc sigs are no longer considered recent. { /' get loc sigs from the Location 08 having both big absolute and relative errors (in comparison to other DB nearby lac sigs) "/ relatively_big_errors_bag <--- get “error_recs" in "error_rec_set” wherein each “error_rec.error" has a size largerthan. for example, the second standard deviation from the mean (ave rage) of such errors; big__errors_bag < get “error_recs" in "relatively_big_errors__bag" wherein each “error__rec.error" has a value larger than "big_error_threshold"; DB_loc_sigs_w_big_errors < get the lot sigs lor “error_recs" in “big__errors_bag” wherein each |oc sig gotten here is identified by “error_rec.loc_sig__id"; /' get /ac sigs from the Location 03 that have been recently added or updated "/ recent__loc_sigs < get_ recent_ /oc__sigs(loc_sig_bag, recent__time); /* Note, the function, “get_recent_loc_sigs" can have various embodiments, including determining the recent location signatures by comparing their time stamps (or other time related measurements) with one or more threshold values for classifying location signatures into a "recent" category returned here and an a category for “old" or updatable location signatures. Note that these categories can be determined by a (tunable) system time threshold parameter(s) for determining a value for the variable, “recent_time", and/or. by data driving this categorization by, e.g., classifying the location signatures according to a standard deviation, such as defining the “recent" category as those location signatures more recent than a second standard deviation of the timestamps oi the location signatures in “loc_sig_bag". ‘I /* subtract the recent lac sigs from the lac sigs with big errors to get the bad ones */ bad_DB_loc_sigs < (big_error_DB_loc_sigs) ~ (recent_loc_sigs); /" lower the confidence of the bad lac sigs */ for each loc_sig[i] in bad_DB_|oc_sigs do loc__sig[i].confidence < (loc_sig[i].confidence) * (confidence_reduction_factor); I46 5 I0 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/U S97] 15892 /* for each had loc 5134 update it in the D8 or remove it from use if its confidence is too low "/ /" Now delete any loc sigs from the DB whose confidences have become too low. '/ for each loc_sig[i] in bad_DB_loc_sigs do if (loc_sig[i].confidence < min loc sig confidence) then { R5170!/[_ /I0/'I_ I/J'£(loc_sig[r"j); /* update composite location objects to reflect a removal of a referenced loc sig'/ verified_loc_entity < rerrri;-ve_ compo:/'re_ /ocar/27/r_ e/It/'ry_ /rav/'ng(loc_sig[i]); /* This gets all other (if any) loc sigs for the composite location object that were verified at the same time as “loc_sig[T]". Note, these other loc sigs may not need to be deleted (i.e., their signal characteristics may have a high confidence); however, it must be noted in the DB, that for the DB composite location entity having “loc_sig[fl", this entity is no longer complete. Thus, this entity may not be useful as. e.g., neural net training data. "/ mark "verified_loc__entity" as incomplete but keep track that a loc sig did exist for the BS associated with "loc_sig[i]"; if (“verified_loc_entity" now references no loc sigs) then ll’!/‘/0V{_ M0/I_ I/J'£(veril"red__loc__entity); } else 0B_ upo’ate_ e/rt/y(loc_sig[fl); // with its new confidence } ENDOF reduce_bad_DB_loc_sigs increase_confidence__of_good_DB__|oc_sigs(nearby_loc_sig_bag, error_rec__set, sma|l__error_threshold, confidence__increase_factor, recent__time); 1"’ This program increases the confidence of verified DB Inc sigs that are (seemingly) of higher accuracy (i.e., in agreement with comparable loc sigs in the DB). Note that the steps herein are also provided in flowchart form in Figs. l 93 through l9b. Inputs: loc_sig__bag: The loc sigs to be tested for determining if their conlidences should be increased. error__rec_set: The set of “error__recs" providing information as to how much each loc sig in “loc_sig__bag" disagrees with comparable loc sigs in the DB. That is, time is an “error rec”lIere /0I'£'2(/I /or:/jein ll /or sig bag? smal|__error_thresho|d: The error threshold below which the errors are considered too small to ignore. confidence_increase__factor. The factor by which to increase the confidence of loc sigs. I47 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/ 15892 recent__time: Time period beyond which loc sigs are no longer considered recent. /* get lac sljgs from the Location 05 having both small absolute and relative errors (in comparison to other DB nearby Ioc sigs) "/ relatively__sma|l_errors_bag < get “error_recs” in "error_rec_set" wherein each “error_rec.error" has a size smaller than. for example, the second standard deviation from the mean (average) of such errors; small_errors_hag < get “error_recs" in “re|atively_smal|_errors_bag" wherein each "error__rec.error" has a size smaller than “sma|l_error__threshold"; DB_|oc_sigs__w_sma||_errors < get the loc sigs lor "error_recs" in “small_errors_bag" wherein each loc sig gotten here is identified by “error_rec.loc_sig_id"; /' get Ioc sigs from the Location DB that have been recently added or updated "/ recent_loc_sigs < get_ recent_ /oc_:/gs(loc_sig_bag. recent_time); /' subtract the recent Ioc sljgs from the lac slfgs with small errors to get the good ones “/ good__DB__|oc_sigs < (smal|_error_DB_loc_sigs) - (recent_loc_sigs); /" for each good Ioc srjg, update its confidence “/ for each loc__sig[i] in good_DB_loc__sigs do { |oc_sig [i] confidence <--- (loc_sig[i].conlidence) "* (conlidence_increase_lactor); il (loc_sig[i].conlidence > l.0) then loc_sig[i] <--. l.0; } ENDOF increase _good_DB_loc_sigs DATA BASE PROGRAMS FOR DETERMINING THE CONSISTENCY OF LOCATION HYPOTHESES WITH VERIFIED LOCATION INFORMATION IN THE LOCATION SIGNATURE DATA BASE LOW LEVEL DATA BASE PROGRAMS FOR LOCATION SIGNATURE DATA BASE I’ The lollowing program compares: (al) loc sigs that are contained in (or derived from) the loc sigs in "target__loc_sig_bag" with (bl) Ioc sigs computed from verilied Ioc sigs in the location signature data base. That is. each Ioc sig from (al) is compared with a corresponding loc sig lrom (bl) to obtain a measurement ol the discrepancy between the two loc sigs. In particular, assuming each of the loc sigs lor “target_loc_sig_bag" correspond to the same target MS location, wherein this location is "target_loc", this program I48 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/15892 determines how well the lot sigs in “target_loc_sig_bag" lit with a computed or estimated loc sig for the location, “target_loc" that is derived from the verified loc sigs in the location signature data base. Thus. this program may be used: (a2) lor determining how well the loc sigs in the location signature cluster [or a target MS (“target_loc_sig_bag") compares with lot sigs derived fmm verified location signatures in the location signature data base. and (b2) for determining how consistent a given collection of lot sigs ("target_loc_sig_bag") from the location signature data base is with other Ioc sigs in the location signature data base. Note that in (b2) each of the one or more loc sigs in “target__|oc__sig_bag" have an error computed here that can be used in determining ii the loc sig is becoming inapplicable for predicting target MS locations Note that the steps herein are also provided in l Iowchart form in figs. 203 through 20d.*'/ Determine_Location_Signature__Fit_Errors(target_loc, target__|oc_sig__bag, search_area, search_criteria, output__criteria) I‘ Input: target__|oc: An MS location or a location hypothesis for a particular MS. Note. this can be any of the following: (a) An MS location hypothesis in which case, the loc sigs in “target_loc_sig_bag" are included in a location signature cluster lrom which this location hypothesis was derived. Note that if this location is inaccurate, then “target_loc_sig_bag" is unlikely to be similar to the comparable loc sigs derived from the loc sigs of the location signature data base close "target_loc”; or (b) A previously verified M5 location, in which case, the loc sigs of “target_loc__sig__bag" are previously verified loc sigs. However, these loc sigs may or may not be accurate now. target_loc_sig_bag: Measured location signatures (“loc sigs" for short) obtained from the particular MS (the data structure here, bag, is an aggregation such as array or list). The location signatures here may be verified or unverilied. However, it is assumed that there is at least one Ioc sig in the bag. Further, it is assumed that there is at most one loc saw per Base Station. It is also assumed that the present parameter includes a “type” field indicating whether the lac srys here have been individually selected, or, whether this parameter references an entire (verified) lac srjg cluster; i.e., the type field my have a value or? "UillV£li'll-‘ll-‘D I.0C SIG CLUSTER" or "VERIFIED LOC SIG CLlI.fTEk'§’ search_area: The representation ol the geographic area surrounding “target_loc”. This parameter is used for searching the Location Signature data base for verified Ioc sigs that correspond geographically to the location of an MS in “search_area"; 20 25 30 CA 02265875 1999-03-09 we 98,1030-, PCT/US97/15892 searclt_criteria: The criteria used in searching the location signature data base. The criteria may include the following: (a) "USE ALL LDC SlGS IN DB”, (b) “USE ONLY REPEATABLE LOC SlGS", (c) “USE ONLY LOC SlGS WITH SIMILAR TIME OF DAY". llowever, environmental characteristics such as: weather, traffic, season are also contemplated. output_criteria: The criteria used in determining the error records to output in "error_rec". The criteria here may include one of: (a) “OUTPUT ALL POSSIBLE Ellll0ll_llECS": (b) “OUTPUT Ellll0ll_llECS F0ll INPUT LOC SlGS 0llLY". Returns: error_rec: A bag of error records or objects providing an indication of the similarity between each Ioc sig in “target_loc_sig_bag" and an estimated loc sig computed for “target_loc" from stored Ioc sigs in a surrounding area of “target_loc". Thus, each error record/object in "error_rec” provides a measurement of how well a loc sig (i.e., wireless signal characteristics) in “target__|oc_sig_bag" (for an associated BS and the MS at “target_|oc") correlates with an estimated Ioc sig between this BS and MS. Note that the estimated Ioc sigs are determined using verified location signatures in the Location Signature DB. Note, each error record in "error_rec" includes: (a) a BS ID indicating the base station to which the error record corresponds; and (b) an error measurement (> =0), and (c) a confidence value (in [0, l]) indicating the confidence to be placed in the error measurement. Also note that since “error__rec" is an aggregate data type (which for many aggregate identifiers in this specification are denoted by the suffix “_bag" on the identifier), it can be any one of a number data types even though it's members are accessed hereinbelow using array notation. */ /" get 35'’: associated with DB Ioc sigs in "search_ area ” that satisfy '%earch__ criteria ” */ DB__loc_sig__bag < retn'eve_ veri/79:/_ /ac_ s/gs(search_area, search__criteria); // get all verified appropriate location signatures residing in the Location Signature data base. // Note, some loc sigs may be blocked from being retrieved. DB_BS_bag < get_ BIs(DB_|oc_sig_bag); // get all base stations associated with at least one location // signature in DB_|oc_sig_bag. Note, some of these BSs may below power "location // BSs". /" get B5"s associated with Ioc sigs in "target_ /oc__ sig_ bag” "/ target_BS_bag < get_ l9.l'r(target_loc_sig__bag); // get all base stations associated with at least one // location signature in “target_loc_sig_bag". /" determine the B3’: /or which error records are to be computed */ lS0 10 20 25 30 CA 02265875 1999-03-09 W0 98/“B07 PCT/US97/15892 case of “output_criteria" including: “OUTPUT ALL POSSIBLE ERROR_llECS": /* In this case, it is desired to determine a collection or error records wherein there is an error record lor each BS that is associated with a Ioc sig in “target_loc_sig__bag" and for each BS associated with a loc in the “search_area" satislying “search_criteria”. "I BS_bag < (DB_BS_bag) union (target_BS_bag); “OUTPUT EllROR_RECS FOR INPUT LOC SIGS ONLY": BS_bag < target_BS_bag; endcase;" /' for each 85' to have an error record computed, make sure there are two Ioc sigs to compare: one Ioc sig derived from the '‘B5'_ bag” Ioc sljg data, and one from derived from the loc srjgs in the Location Signature 05, wherein both /ac sigs are associated with the location, “target_Ioc”. */ for each BS[i] in “BS_bag" do { /* determine two (estimated) loc sigs at “target__loc", one derived from “target_loc_sig_bag" (if possible) and one derived from Location Signature DB Ioc sigs (if possible) ‘I comparison_loc__sig_bag[i] <--- /etrieve_ var/'/1erI__/ar_s4as_/oI(BS[i].search_area, search__criteria); /* get all loc sigs iorwhich BS[i] is associated and wherein the verified MS location is in “search_area" (which surrounds the location “target_|oc”) and wherein the loc sigs satisfy “search_criteria". "/ /* now determine if there are enough Ioc sigs in the “comparison__|oc__sig_bag” to make it worthwhile to try to do a comparison. ‘I it ( (SIZEOf(comparison_loc_sig_bag[i])/(S|lE0f(search_area))) < min_threshold_ratio(area_cype(search__area))) then /* it is believed that there is not a dense enough number of verified Ioc sigs to compute a composite Ioc sig associated with a hypothetical MS at “target__|oc". */ error_rec[i].error < invalid; else /" there are enough Ioc sigs in “comparison_ Ioc_ sig_ bag" to continue, and in particular; an estimated Ioc sig can be derived from the lac srjgs in "comparison_ lac_ s:g_ bag”; however; first see if a target Ioc sig can be determined; if so, then make the estimated lac sig (denoted “estimz ted_ Ioc__ sig[I]”) . */ 20 25 30 CA 02265875 1999-03-09 W0 98,1030-7 PCTIU S97/ 15892 il (BS[i] is in target_BS_bag) then /“' get a lot sig in "target_BS_bag” lor BS[i]; assume at most one lot sig per BS in “target__loc__sig__bag" */ target_loc_sig{i] < ger_ /o(__ 5/g(BS[i], target_loc_sig_bag); else /"“ BS[i] is not in “target_BS__bag”, accordingly this implies that we are in the process of attempting to output all possible error records (or all BS’s: (a) that have previously been detected in the area of “search_area" (satislying “search_criteria"), union. (b) that are associated with a lot sig in "target_|oc_sig_bag". Note, the path here is performed when the MS at the location lor “target_loc" did not detect the BS[fl, but 8S[i] has previously been detected in this area. */ il (target_loc_sig_bag.type = = “UNVERIFIED LOC SIG CLUSTER") then /* can at least determine if the NS for the cluster detected the BS [I]; i.e., whether 8S[i] was in the set of BS's detected by the MS even though no loc sig was obtained for BS[i]. */ if (B.[_ on/)'_ dereaeo(target_loc_sig_bag, 8S[i]) ) then I‘‘‘ detected but no Ioc sig */ error_rec[i].error < invalid; /* can't determine an error if this is all the information we have */ else /* BS[i] was not detected by the MS at “target_loc.location", so the pilot channel lor BS[i] was in the noise; make an artificial loc sig at the noise ceiling (alternatively, e.g., a mean noise value) for the MS location at “target_|oc" ‘I target_loc_sig[i] < gr-r_ no/Zre_ re/7/'ng_ /or_ J/g(target_loc); else; I‘ do nothing; there are no other types lor "target_loc_sig_bag.type” that are currently used when outputting all possible error records for BS's */ il (error_rec[i].error NOT invalid) then /* we have a “target_|oc_sig" lor comparing, so get the derived Ioc sig estimate obtained from the verified lot sigs in the location signature data base. */ estimated__loc_sig[i] < estimate_loc_sig_from_DB(target_lot.location, comparison_loc_sig_bag[i]); /* lhe above call function provides an estimated Ioc sig for the location of “target_loc" and BS[i] using the verified loc sigs of “comparison_loc__sig_bag[i]” "/ } /" /or each 85 whose error record has not been marked "invalid '1 both “target; loc_ srjg” and ‘‘estimated_ loc_ srgr” are now well-defined; so compute an Us 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT /U S97/ 15892 error record related to the difference between "target; loc_ 513” and “estimated__loc_sig”. */ for each BS[i] in “BS_bag " with error_rec[i].error not invalid do /'* determine the error records for these base stations */ { /* Note, the "target__|oc_sig” here is for an MS at or near the location for the center of area for "target_loc“. */ error_rec[r"] < get_difference_measurement(target_loc_sig[i]. estimated_loc_sig[fl, comparison_|oc_sig_bag[t'j, search_area. search_criteria);/* get a measurement of the difference between these two loc sigs. */ error_rec.loc_sig_id <--- target_|oc_sig[i].id; /* this is the loc sig with which this error_rec is associated */ error_rec.compariso n_loc_sig_id_bag < compariso n_|oc_sig_bag[i]; } RETllRN(error_rec); ENDOF Determine__l.ocation__Signature__Fit__Errors estimate_loc_sig_from_DB(loc___for___estimation, Ioc___sig_bag) /' This function uses the verified loc sigs in “loc__sig_bag" to determine a single estimated (or "typical") loc sig derived from the loc sigs in the bag. Note, it is assumed that all loc sigs in the “|oc_sig_bag" are associated with the same BS |22 (denoted the BS associated with the “loc_sig_bag") and that the locations associated with these loc sigs are near “|oc_for_estimation”. Further, note that since the loc sly: are verified, the associated base station was the primary base station when the lac sig signal measurements were sampled. Thus. the measurements are as precise as the infrastructure allows. Note that the steps herein are also provided in flowchart form in Fig. 2f. Input: loc_lor_estimation A representation of a service area location. loc_sig_bag A collection of verified loc sigs, each associated with the same base station and each associated with a service area location presumably relatively near to the location represented by “loc_lor_estimation". '/ est_loc_sig < extrapolate/interpolate a location signature for the location at “loc_for_estimation” based on loc sigs in “loc_sig_bag”; /" Note, “est_loc_sig" includes a location signature and a confidence measure. The confidence measure (in the range: [0, l]) is based on: (a) the number of verified loc sigs in the search area; (b) how well they surround the center location of the new__loc, and (c) the confidence factors of the loc sigs in "|oc_sig_bag" (e.g., use average confidence value). I53 CA 02265875 1999-03-09 WO 98110307 PCT/US97I1S892 Note, for the extrapolation/interpolation computation here, there are many such extrapolation/interpolation methods available as one skilled in the art will appreciate. For example, in one embodiment of an extrapolationfinterpolation method. the following steps are contemplated: (39.l) Apply any pre-processing constraints that may alter any subsequently computed “est_loc_sig" values derived below). For example, if the BS associated with "loc_sig_bag" is currently inactive "location BS" (i.e., “active” meaning the BS is on-line to process location information with an MS, "inactive" meaning the not on-line), then, regardless of any values that may be determined hereinbelow, a value or flag is set (for the signal topography characteristics) indicating “no signal" with a confidence value of I is provided. further, additional pre- processing may be performed when the BS associated with “loc_sig_bag" is a location BS (LBS) since the constraint that a pilot channel from such an LBS is likely to be only detectable within a relatively small distance from the BS (e.g., [000 ft). For example, if the MS location. “loc__for_estimation", does not intersect the radius (or area contour) of such a location BS, then, again ,a value or flag is set (for the signal topography characteristics) indicating “outside of LBS area" with a confidence value of l is provided. Alternatively, if (a) a determined area, A, including the MS location. “loc_for_estimation" (which may itself be, and likely is, an area). intersects (b) the signal detectable area about the location BS. then (c) the confidence factor value may be dependent on the ratio of the area of the intersection to the minimum of the size of the area in which the LBS is detectable and the size of the area of “loc_for__estimation”, as one skilled in the art will appreciate. Further, it is noteworthy that such pre-processing constraints as performed in this step may be provided by a constraint processing expert system, wherein system parameters used by such an expert system are tuned using the adaptation engine I382. (39.2) Assuming a value of “no signal" or “outside of LBS area" was not set above (since otherwise no further steps are performed here). for each of the coordinates (records), C, of the signal topography characteristics in the loc sig data structure, generate a smooth surface, S(C), of minimal contour variation for the set of points { (x.y.z) such that (x,y) is a representation of a service area location , and z is a value of C at the location (x,y) for some Ioc sig in “loc_sig_bag" wherein (x,y) is a point estimate (likely centroid) of the Ioc sig}. Note that a least squares technique, a partial least squares technique, or averaging on “nearby" (x,y,z) points may be used with points from the above set to generate other points on the surface S(C). Additionally, note that for at least some surfaces characterizing signal energy, the generation process for such a surface may use the radio signal attenuation formulas for urban, suburban, and rural developed by M. Hata in IEEE Trans, VT-29, pgs. 317-325, Aug. I980, "Empirical Formula For Propagation Loss in Land Mobile Radio" (herein incorporated by reference). For example, Hata’s formulas may be used in: (39.2.l) Determining portions of the surfaces S(C) where there is a low density of verified Ioc sigs in “loc_sig_bag". In particular. if there is a very low density of verified loc sigs in “loc_sig_bag" lor the service area surrounding the location of “loc__lor_estimation". then by determining the area I54 10 15 20 25 30 W0 98/ 10307 CA 02265875 1999-03-09 PCT/US97ll 5892 type(s) (e.g.. transmission area type as described hereinabove. assuming a .correspondence between the transmission area types and the more coarse grained categorization of : urban, suburban, and rural) between this location and the base station associated with “loc_sig_bag”. and applying Hata‘s corresponding formula(s), a signal value 2 may be estimated according to these type(s) and their corresponding area extents between the MS and the BS. Note, however, that this option is considered less optimal to using the verified loc sigs of “loc__sig_bag” for determining the values of a surface S(C). Accordingly, a lower confidence value may be assigned the resulting composite loc sig (i.e., “est_loc_sig”) determined in this manner; and relatedly, (39.2.2) Determining a surface coordinate (xo,yo,z0) of S(C) when there are nearby verified loc sigs in “loc_sig_bag". for example, by using Hata's formulas, an estimated surface value I} at the location (x°,y,,) may be derived from estimating a value 2-, at (x°,yo) by adapting Hata’s formula’: to extrapolate/interpolate the value 2; from a nearby location (x.,y~,) having a verified loc sig in “loc_sig_bag". Thus, one or more estimates 2-, may be obtained used in deriving 20 as one skilled in statistics will appreciate. Note. this technique may be used when there is a moderately low density of verified loc sigs in "loc_sig_bag" for the service area surrounding the location of “loc_for_estimation". However, since such techniques may be also considered less than optimal to using a higher density of verified loc sigs of “|oc_sig_bag" for determining the values of a surface S(C) via a least squares or partial least square technique, a lower confidence value may be assigned the resulting composite loc sig (i.e., "est_loc__sig”) determined in this manner. further. recall that the values. 1. for each loc sig are obtained from a composite of a plurality of signal measurements with an MS, and, that each value 1 is the most distinct value that stands out above the noise in measurements for this coordinate, C. So, for example in the CDMA case. for each of the coordinates C representing a finger of signal energy from or to some MS at a verified location, it is believed that S(C) will be a smooth surface without undulations that are not intrinsic to the service area near “|oc_for_estimation”. (39.3) for each of the coordinates. C. of the signal topography characteristics, extrapolatefinterpolate a (I-coordinate value on $02) for an estimated point location of “loc_for_estimation". Further note that to provide more accurate estimates, it is contemplated that llata’s three geographic categories and corresponding formulas may be used in a fuzzy logic framework with adaptive mechanisms such as the adaptation engine l382 (lor adaptively determining the fuzzy logic classifications). hdditionally, it is also within the scope of the present invention to use the techniques of L. E. Vogler as presented in “The Attenuation of Electromagnetic Waves by Multiple Knife Edge Diffraction”, US Dept of Commerce, Nl'lA nos, 8|-86 (herein incorporated by reference) in the present context for estimating a loc sig between the base station associated with “loc_sig_bag" and the location of “loc_for_estimation". *‘/ llETllllN(est_loc_sig ) I55 10 15 20 30 WO 98/10307 CA 02265875 1999-03-09 PC T/U S97/ 1 5892 ENDOF estimate_loc_sig_from_DB get_area_to_search(|oc) I‘ This function determines and returns a representation of a geographic area about a location, “loc". wherein: (a) the geographic area has associated MS locations for an acceptable number (i.e.. at least a detennined minimal number) of verified loc sigs lrom the location signature data base. and (b) the geographical area is not too big. However, if there are not enough loc sigs in even a largest acceptable search area about "lac", then this largest search area is returned. Note that the steps herein are also provided in llowchart form in figs. 22a through 22b. */ { loc_area_type < gt-r_ a/ea_ rype(loc); /*get the area type surrounding “loc"; note this may be a vector of fuzzy values associated with a central location of “loc", or, associated with an area having "lot". */ search_area <--- ger_ de/at//t_ area_about(loc); /* this is the largest area that will be used ‘I saved_search_area < search_area: // may need it alter “search_area" has been changed search_area_types <--- get_area_ rypes(search_area); // e.g., urban. rural. suburban. mountain,etc. loop until llflllllll performed: { min_acceptable_nbr_loc_sigs < 0; // initialitation for each area__type in “search_area_types" do { area_percent <--- ger_perce/rr__ of_ area_ ai(area_type, search_area); /" get percentage ol area having “area_type" "/ min_acceptable_nbr_loc_sigs < min_acceptab|e_nbr_loc_sigs + [(get_min__acceptable_verifed_loc__sig__density_lor(area_type)) * (Sl1EOF(search__area) " area_percentt / l00)]; } I*’ Now get all verified loc sigs iron: the location signature data base whose associated MS location is in "search_area". '/ total__nbr_loc_sigs < get_a//_ ver/f/'ed_ 0B_ /01; s/grs(search_area); if (min_acceptable_nbr_loc__sigs > total_nbr_loc_sigs) then /"‘ not enough loc sigs in “search_area"; so return “saved__search_area" */ RETURN(saved_search_area); else /’~“ there is at least enough loc sigs, so see if “search_area" can be decreased */ { saved_search_area <--- searth__area; I56 20 25 30 CA 02265875 1999-03-09 W0 98I10307 PC'I'/US97/ 15892 search_area < o'ecrea:e__ searr/I_ a/ea_ abor/I(loc, search_area); } ENDOF get_area_to_search l" for processing various types of loc sigs, particular signal processing filters may be required. Accordingly, in one embodiment of the present invention. a “filter_bag" object class is provided wherein various filters may be methods of this object (in object-oriented terminology) for transforming loc sig signal data so that it is comparable with other loc sig signal data from, for example, an MS of a different classification (e.g., different power classification). it is assumed here that such a “fi|ter_bag" object includes (or references) one or more filter objects that correspond to an input filter (from the Signal filtering Subsystem I220) so that, given a location signature data object as input to the filter_bag object, each such filter object can output loc sig filtered data corresponding to the filter object's filter. Note, such a filter__bag object may accept raw loc sig data and invoke a corresponding filter on the data. Further, a filter_bag object may reference filter objects having a wide range of filtering capabilities. for example, adjustments to loc sig data according to signal strength may be desired for a particular loc sig comparison operator so that the operator can properly compare MS's of different power classes against one another. Thus, a filter may be provided that utilizes, for each BS, a corresponding signal strength change topography map (automatically generated and updated from the verified loc sigs in the location signature data base l320) yielding signal strength changes detected by the BS for verified MS location‘: at various distances from the BS, in the radio coverage area. Additionally, there may also be filters on raw signal loc sig data such as quality characteristics so that loc sigs having different signal quality characteristics may be compared. '/ get_difference__measurement(target_loc_sig, estimated_|oc_sig, comparison_|oc__sig_bag, search_area, search_criteria) /"‘ Compare two location signatures between a BS and a particular MS location (either a verified or hypothesized location) for determining a measure of their difference relative to the variability of the verified location signatures in the “comparison_|oc__sig_bag" from the location signature data base I320. Note, it is assumed that "targe-t__lac_sig”, "estimated_ loc_ sig” and the lac sigs in "comparison_ lac__sig_ bag” are all associated with the same 83' I22. lloreover, it is assumed that “target_|oc_sig"and "estimated_|ac_sig" are well-defined non-llll loc sigs. and additionally, that “comparison_loc_sig_bag" is non-NIL fhis function returns an error record, “error_rec", having an error or difference value and a confidence value for the error value. Note, the signal characteristics of “target_loc_sig" and those of “estimated_|oc_sig” are not assumed to be normalized as described in section (26.l) prior to entering this function so that variations in signal characteristics resulting from variations in (for example) MS signal processing and generating characteristics of different types of lfS's may be reduced, as described in the discussion of the loc sig data type hereinabove. ft is further assumed I57 CA 02265875 1999-03-09 W0 98/ 10307 PCT /U S97! 15892 that typically the input loc sigs satisfy the “search_criteria". Note that the steps herein are also provided in flowchan lorm in figs. 23a through 23c. target___loc__sig: The loc sig to which the "error_rec" determined here is to be associated. Note that this loc sig is associated with a location denoted hereinbelow as the “particular location”. estimated_loc_sig: The loc sig to compare with the “target_loc_sig", this loc sig: (a) being lor the same MS location as “target_loc_sig”, and (b) derived from verified loc sigs in the location signature data base whenever possible. However, note that if this loc sig is not derived from the signal characteristics of loc sigs in the location signature data base, then this parameter provides a lot sig that corresponds to a noise level at the particular MS location. comparison_loc_sig_bag: The universe of loc sigs to use in determining an error measurement between “target_loc_sig” and "estimated__loc_sig" . Note, the loc sigs in this aggregation include all loc sigs lor the associated Base Station I22 that are in the “search_area" (which surrounds the particular MS location for “target_|oc_sig") and satisfy the constraints of "search_criteria". It is assumed that there are sufficient loc sigs in this aggregation to perform at least a minimally effective variability measurement in the loc sigs here. search_area: A representation of the geographical area surrounding the particular MS location for all input loc sigs. This input is used for determining extra information about the search area in problematic circumstances. searcl1_criteria: The criteria used in searching the location signature data base I320. The criteria may include the following: (a) “USE ALL LOC SIGS lll DB", (b) “USE ONLY REPEATABLE LOC SlGS", (c) “USE ONLY LOC SIGS WITH SIMILAR TIME OF DAY However, environmental characteristics such as: weather, tralfic, season are also contemplated. "‘/ error <--- 0: // initialization /" get identifiers for the filters to be used on the input lac sigs "/ filter_bag <--- ger_/i/ter_ob/l-(1.;/a(_dimverrcg/rreasr//e/ne/rt(target_loc__sig, estimated_loc__sig, comparison_|oc_sig__bag); I’ It is assumed here that each entry in “filter_bag" identifies an input filter to be used in the context of determining a diflerence measurement between loc sigs. Note, if all loc sigs to be used here are of the same type, then it may be that there is no need for filtering here. Accordingly, "lilter_bag" can be empty. Alternatively, there maybe one or more filter objects in “filter_bag".*/ 20 25 30 CA 02265875 1999-03-09 wo 98I10307 PCT/US97/15892 /’ initialization: ’/ /' for each filter, determine a difierence measurement and confidence "/ for each li|ter_obj indicated in filter_bag-do { /' filter "target__ loc_sig”, "estimated__ Ioc_sig” and lo: sigs in "comparison__ lac_sig_ bag"; note, each filter; ob/' can determine when it needs to be applied since each lot sig includes: (a) a description of the type (e.g., make and model) of the loc sig's associated M5, and (b) a filter flag(s) indicating fiIter(s) that have been applied to the loc sig. "/ target_loc_sig < filter__obj(target_loc_sig); /* filter at least the signal topography characteristics */ estimated_loc_sig < -»- filter__obi(estimated_loc_sig); /* filter at least the signal topography characteristics ‘I comparison_loc_sig_bag<--- lilter_obj(comparison_Ioc_sig_bag); /“ filter loc sigs here too */ /' determine a difierence measurement and confidence for each signal topography characteristic coordinate */ for each signal topography characteristic coordinate, C, ol the loc sig data type do { variability_measmt.vaI <-.- ger_ var/abr7iIy_range(C, comparison_loc_sig_bag); /"' This function provides a range of the variability of the C-coordinate. In one embodiment this measurement is a range corresponding to a standard deviation. However, other variability measurement definitions are contemplated such as second. third or fourth standard deviations. "‘/ /" make sure there are enough variability measurements to determine the variability of values for this coordinate. */ if (SIZEOF(comparison_|oc__sig_bag) < expec1ed__B!_ /ac__.rig_ t/In-.rlIolo(search_area. search_criteria)) then /’ use the data here, but reduce the confidence in the variability measurement. Note that it is expected that this branch is performed only when “comparison_loc_sig_bag" is minimally big enough to use (since this is an assumption for performing this function), but not of sufficient size to have full confidence in the values obtained. Note. a tunable system parameter may also be incorporated as a coefficient in the computation in the statement immediately below. In particular. such a tunable system parameter may be based on "search_area" or more particularly, area types intersecting “search_area".*/ { 20 CA 02265875 1999-03-09 wo 93/10307 PCT/US97l 15892 variabi|ity_measmt_conf_reduttion_factor < SIZEOf(comparison_|oc_sig_bag)/ expeaea’_BJ'_/oc_sig__rl:res/Io/o(search_area.searth_criteria); } else /* There is a sufficient number of lot sigs in “comparison_ioc_sig_bag" so continue */ { variabi|ity_measmt_coni_reduction_factor < L0; //i.e., don't reduce confidence } /"' Now determine the C -coord difference measurement between the “target_ loc__sIjg” and the "estimated_ lac__sig” "/ delta < AB5(target_|ot_sig[C] - estimated_|oc_sig[C]); // get absolute value of the difference if (delta > variabiiity_measmt.vaf) then { error <.-- error + (deIta/variability_measmt.va|); } }I’ end C~coord processing '‘‘l /’ construct the error record and return it "/ error_rec.error < error; /' Get an average confidence value for the lac slgs in "comparison__lac_srg__ bag" Note, we use this as the confidence of each lac sig coordinate below. '/ average_confidente < AV£M6[(|oc_sig.confidence for |oc_sig in “tomparison_|oc_sig_bag”); error_rec.confidence <--- MIN(target_|oc_sig.confidence. estimated_|oc_sig.confidence, (average_confidence * variabi|ity_measmt_tonf_reduction_factor)); // presently not used RETURN(error_rec); ENDOF get_difference__measurement 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/ 15892 APPENDIX D: Context Adjuster Embodiments A description of the Iugb level functions in a first embodiment of the Context Adjuster co ntext_adiuster(loc_hyp_list) /* This function adjusts the location hypotheses on the list, “loc__hyp_list”, so that the confidences of the location hypotheses are determined more by empirical data than delault values from the first Order Models I224. That is. for each input location hypothesis, its confidence (and an MS location area estimate) may be exclusively determined here if there are enough verified location signatures available within and/or surrounding the location hypothesis estimate. This function creates a new list of location hypotheses lrom the input list, “loc_hyp_list". wherein the location hypotheses on the new list are modified versions ol those on the input list for each location hypothesis on the input list, one or more corresponding location hypotheses will be on the output list. Such corresponding output location hypotheses will diller lrom their associated input location hypothesis by one or more of the lollowing: (a) the “image_area" field (see Fig. 9) may be assigned an area indicative of where the target MS is estimated to be, (b) if “image_area" is assigned. then the "confidence" field will be the confidence that the target MS is located in the area for "image_area", (c) if there are not sulficient “nearby” verified location signature clusters in the location signature data base to entirely rely on a computed confidence using such verified location signature clusters. then two location hypotheses (having reduced confidences) will be returned, one having a reduced computed confidence (lor “image__area") using the verified clusters in the location Signature DB. and one being substantially the same as the associated input location hypothesis except that the confidence (lor the field "area_est”) is reduced to reflect the confidence in its paired location hypothesis having a computed confidence for “image_area". Note also. in some cases, the location hypotheses on the input list, may have no change to its confidence or the area to which the confidence applies. Note that the steps herein are also provided in llowchart form in figs. 25a and 25b. */ { new_loc_hyp_list < c/t°ate_new_ empty_ /in'(); for each loc_hyp[i] in loc_hyp__list do /* Note, is a first Order Model I224 indicator. indicating the model that output “hyp_loc[i]” ‘I re/rIove__ /ram_//3r(loc_hyp[i]. loc__hyp__list); it (NOT loc_hyp[i].adjust) then I" no adjustments will be made to the “area_est” or the “conlidence" fields since the “adjust" field indicates that there is assurance that these other fields are correct; note that such designations indicating that no adjustment are presently contemplated are only for the location hypotheses generated by the Home Base Station first Order Model, the location Base Station first Order Model and the Mobil Base l6l I0 15 20 25 30 CA 02265875 1999-03-09 WO 98110307 PCT/US97I 15892 Station first Order Model. ln particular, location hypotheses from the Home Base Station model will have confrdences of L0 indicating with highest confidence that the target MS is within the area estimate for the location hypothesis. Alternatively, in the Location Base Station model, generated location hypotheses may have conlidences of (substantially) + L0 (indicating that the target MS is absolutely in the area for “area_est"), or, -|.0 (indicating that the target MS is NOT in the area estimate for the generated location hypothesis).*/ { loc_hyp[i].image_area <--- NULL: // no adjustment, then no “image_area" add_to_|ist(new_loc_hyp_|ist, |oc_hyp[i]); // add "loc_hyp[i]" to the new list } else /* the location hypothesis can (and will) be modified; in particular, an “image_area" may be assigned. the “confidence" changed to reflect a confidence in the target MS being in the “image_arca”. Additionally, in some cases, more than one location hypothesis may be generated from “loc_hyp[i]". See the comments on FIG. 9 and the comments for “get_adjusted_|oc_hyp_list_lor" for a description of the terms here. “l { temp__list < get__:|diusted_loc_hyp__|ist_for(loc_hyp[i]); new_loc_hyp_list < co/rrbi/1e_fi}tr(new_|oc_hyp_list. temp_list); } RETURN(new_|oc__hyp__|ist); }EllDOF get__adjusted__loc__hyp_list_for(|oc_hyp) /' This function returns a list (or more generally, an aggregation object) of one or more location hypotheses related to the input location hypothesis, “loc_hyp". In particular, the returned location hypotheses on the list are “adjusted" versions of “loc_hyp" in that both their target MS ltlll location estimates, and confidence placed in such estimates may be adjusted according to archival MS location information in the location signature data base l320. Note that the steps herein are also provided in flowchart form in figs. 26a through 26c. RETURNS: loc_hyp_list Ihis is a list of one or more location hypotheses related to the input “loc_hyp”. Each location hypothesis on “loc_hyp_|ist" will typically be substantially the same as the input “loc_hyp" except that there may now be a new target MS estimate in the field, “image__area", and/or the confidence value may be changed to reflect information of verified location signature clusters in the location signature data base. Introductory Information Related to the Function. "get__adjusted_foc_hyp__list_for" I62 10 15 20 25 30 CA 02265875 1999-03-09 we 93,1307 PCTIUS97Il5892 This function and functions called by this function presuppose a framework or paradigm that requires some discussion as well as the defining of some terms. Note that some of the terms defined hereinbelow are illustrated in Fig. 24. Define the term the “the cluster set" to be the set of all MS location point estimates (e.g., the values of the “pt_est” field of the location hypothesis data type), for the present FOM. such that these estimates are within a predetermined corresponding area (e.g., “loc_hyp.pt_covering" being this predetermined corresponding area) and these point estimates have verified location signature clusters in the location signature data base. Note that the predetermined corresponding area above will be denoted as the "cluster set area". Define the term “image cluster set" (for a given first Order Model identified by “loc_hyp.f0f1_lD") to mean the set of y_eri_liet1 location signature clusters whose MS location point estimates are in "the cluster set". Note that an area containing the “image cluster set" will be denoted as the “image cluster set area" or simply the “image area" in some contexts. further note that the “image cluster set area" will be a "small" area encompassing the “image cluster set". In one embodiment, the image cluster set area will be the smallest covering of cells from the mesh for the present FOM that covers the convex hull of the image cluster set. Note that preferably, each cell of each mesh for each FOM is substantially contained within a single (transmission) area type. I Thus, the present FOM provides the correspondences or mapping between elements of the cluster set and elements of the image cluster set. */ { add_to_list(loc_hyp_list, loc_hyp); /* note the fields of “loc_hyp" may be changed below, but add "loc_hyp" to the list. “loc_hyp_list here “/ mesh <--- ger_ ce//_me:/r_foLmode.(loc__hyp.f0M_lD); /* get the mesh of geographic cells forthe first Order Model for this location hypothesis.*‘/ pt_min_area <---ger_/ni/r_area__sr/rrau/7o9irg_pI(loc_hyp, mesh); I*‘ Get a minimal area about the MS location point, "pt_est" of "loc_hyp{i]" indicating a point location of the target MS. Note that either the “pt_est" field must be valid or the “area_est" field of “loc_hyp[i]" must be valid. lf only the latter field is valid, then the centroid of the “area_est" field is determined and assigned to the “pt_est" field in the function called here. Note that the mesh of the model may be useful in determining an appropriately sized area. ln particular, in one embodiment. if "loc__hyp.pt_est" is interior to a cell, C, of the mesh, then “pt_min_area" may correspond to C. further note that in at least one embodiment. “pt_min_area” maybe dependent on t/re area rypewithin which "loc_hyp.pt*est” resides, since sparsely populated flat areas may be provided with larger values for this identifier. further, this function may provide values according to an algorithm allowing periodic tuning or adjusting of the values output. via. e.g., a Monte Carlo simulation (more generally. a statistical simulation), a regression or a Genetic Algorithm. for the present discussion, assume: (i) a cell mesh per mil I224; (ii) each cell is contained in substantially a single (transmission) area type; and (iii) “pt_min_area" represents an area of at least one cell. "-"/ area < pt_min’area; // initialization I63 10 15 20 25 30 W0 98/10307 CA 02265875 1999-03-09 PCTIUS97/15892 pt_max_area <---get_ max_area_rwrau/m’/}rg_pr(loc_hyp, mesh); /* Get the maximum area about “pt_est" that is deemed worthwhile for examining the behavior of the “loc_hyp.F0l‘l_lD" first Order Model (POM) about “pt_est“. Note that in at least one embodiment, this value ol this identifier may also be dependent on the area type within which “loc_hyp.pt_est" resides. Further, this function may provide values according to an algorithm allowing periodic tuning or adjusting of the values output, via, e.g., a Monte Carlo simulation (more generally, a statistical simulation or regression) or a Genetic Algorithm. in some embodiments of the present invention, the value determined here may be a relatively large proportion of the entire radio coverage area region. However, the tuning process may be used to shrink this value for (for example) various area types as location signature clusters for verified MS location estimates are accumulated in the location signature data base. */ min_clusters <--- get__ mi/r__/rbr_ af_ c/um/.i(loc_hyp.f0l‘1_lD,area); l*' for the area, "area". get the minimum number (“min_clusters") of archived MS estimates, L, desired in generating a new target MS location estimate and a related confidence, wherein this minimum number is likely to provide a high probability that this new target MS location estimate and a related confidence are meaningful enough to use in subsequent Location Center processing for outputting a target MS location estimate. More precisely, this minimum number, "min_clusters," is an estimate of the archived MS location estimates, L, required to provide the above mentioned high probability wherein each L satisfies the following conditions: (a) L is in the area for “area"; (b) L is archived in the location signature data base; (c) L has a corresponding verified location signature cluster in the location signature data base; and (d) L is generated by the FOM identified by “loc__hyp.f0ff_lD"). In one embodiment, “min_clusters" may be a constant; however. in another it may 1311 accordigto area type antmir area size (of "area"), in some it may also vary according to the FOM indicated by “loc_hyp.F0li_lD". ‘I pt_est_bag <--- ger_ pr_ em_ /or_ image_c/r/.rrer_ .ret(loc_hyp.f0ll_lD, loc_hyp.pt__est, area); /"‘ Get the MS location point estimates for this FOM wherein for each such estimate: (a) it corresponds to a verified location signature cluster (that may or may not be near its corresponding estimate), and (h) each such HS estimate is in "pt_min_area". */ /* Now, if necessary, expand an area initially starting with "pt__min_area” until at least "min_c|usters" are obtained. or, until the expanded area gets too big. "I while ((sizeof(pt_est_bag) < min_clusters) and (sizeol(area) < = pt_max_area) do { area < incre2se(area); min_c|usters < ger_ mi/I_ nbr_ o{_ r/r/.n‘ers(loc_hyp.FOl‘l_lD, area); // update for new "area" pt_est_bag < ger__pt_ e.rr.r__ /ar_image_ c/u.rter_ seI(loc_hyp.FOM__lD, loc_hyp.pt_est, area); } amt/r_ ta(|oc__hyp.pt_covering, area): // Make “area" the “pt_covering" field if (sizeof(pt__est_bag) = = ) then I‘ there aren't any other FOM MS estimates having corresponding verified location signature clusters; so designate "locfihyp" as part of the second set as described above and return. */ I64 U: 10 15 20 25 30 W0 98/ 10307 CA 02265875 1999-03-09 PCTlUS97l15892 { loc_hyp.image__area <--- NULL; // no image area for this loc_hyp; this indicates second set llETURN(loc_hyp_list); } /" It is now assured that "pt_est_bag" is non-empty and "area” is at least the size of a mesh cell. "/ /' Now determine “image_area” field for “loc__hyp" and a corresponding confidence value using the verified location signature clusters corresponding to the MS point estimates of “area” (equivalently, in "pt__est:__bag”). ‘I I‘ There are various strategies that may be used in determining confidences of the “image_area" of a location hypothesis. In particular. for the MS location estimates (generated by the POM of loc_hyp.F0l‘l_|D) having corresponding verified location signature clusters (that may or may not be in "area"), if the number of such MS location estimates in "area" is deemed sufficiently high (i.e., > = "min_clusters" for “area"), then a confidence value can be computed for the "image_area" that is predictive of the target MS being in “image_area". Accordingly. such a new confidence is used to overwrite any previous confidence value corresponding with the target MS estimate generated by the FOM. ihus, the initial estimate generated by the FOM is. in a sense, an index or pointer into the archived location data of the location signature data base for obtaining a new target MS location estimate (i.e., “image_area") based on previous verified MS locations and a new confidence value for this new estimate. Alternatively, if the number of archived FOM MS estimates that are in “area,” wherein each such MS estimate has a corresponding verified location signature clusters (in “image__area"), is deemed too small to reliably use for computing a new confidence value and consequently ignoring the original target MS location estimate and confidence generated by the POM, then strategies such as the following may be implemented. (a) In one embodiment. a determination may be made as to whether there is an alternative area and corresponding “image_area” that is similar to “area" and its corresponding "image_area" (e.g., in area size and type), wherein a confidence value for the “image_area" of this alternative area can be reliably computed due to there being a sufficient number of previous i0l‘l MS estimates in the alternative area that have corresponding verified location signature clusters (in the location signature data base). Thus, in this embodiment, the confidence of the alternative "image__area" is assigned as the confidence for the “image_area" for of “area”. (b) In another embodiment, the area represented by "pt_max_area" may be made substantially identical with the MS location service region. So that in many cases. there will be. as "area" increases. eventually be enough MS location estimates in the cluster set so that at least "min_clusters" will be obtained. Note. a drawback here is that "image_area" may be in become inordinately large and thus be of little use in determining a meaningful target MS location estimate. I65 10 20 25 30 W0 98/10307 CA 02265875 1999-03-09 PCT/US97/15892 (c) ln another embodiment, denoted herein as the two tier strategy, both the original FOM MS location estimate and confidence as well as the "image_area" MS location estimate and a confidence are used. That is, two location hypotheses are provided for the target MS location, one having the FOM MS location estimate and one having the MS location estimate for “image_area". However, the conlidences of each of these location hypotheses maybe reduced to reflect the resulting ambiguity of providing two different location hypotheses derived from the same FOM MS estimate. Thus, the computations for determining the confidence of “image_area may be performed even though there are less than the minimally required archived fold estimates nearby to the original FOM target MS estimate. In this embodiment, a weighting(s) may be used to weight the confidence values as, for example, by a function of the size of the “image_cluster_set". For example, if an original confidence value from the ion was 0.76 and “area" contained only two-thirds of the minimally acceptable number, “min_clusters", then if the computation for a confidence of the corresponding “image_area" yielded a new confidence of 0.43. then a confidence for the original POM target MS estimate may be computed as [ 0.76 * (l/3)] whereas a confidence for the corresponding "image_area” maybe computed as [0.43 " (2/3)]. However, it is within the scope of the present invention to use other computations for modifying the confidences used here. for example, tunable system coefficients may also be applied to the above computed confidences. Additionally. note that some embodiments may require at least a minimal number of relevant verified location signature clusters in the location signature data base before a location hypothesis utilizes the “image_area" as a target MS location estimate. Although an important aspect of the present invention is that it provides increasingly more accurate MS location estimates as additional verified location signatures are obtained (i.e., added to the location signature data base), it may be the case that for some areas there is substantially no pertinent verified location signature clusters in the location signature data base (e.g., "image_area" may be undefined). Accordingly, instead of using the original FOM generated location hypotheses in the same manner as the location hypotheses having target MS location estimates corresponding to “image_areas" in subsequent MS location estimation processing, these two types of location hypotheses may be processed separately. Thus, a strategy is provided, wherein two sets of (one or more) MS location estimates may result: (i) one set having the location hypotheses with meaningful "image_areas" as their target NS location estimates and (ii) a second set having the location hypotheses with their confidence values corresponding to the original sou target MS estimates. Since the first of these sets is considered. in general, more reliable, the second set may used as a “tie breaker" for determining which of a number of possible MS location estimates determined using the first set to output by the Location Center. Note, however, if there are no location hypotheses in the first set, then the second set may be used to output a Location Center target MS location estimate. further note that in determining confidence: of this second set, the weighting of confidence values as described above is contemplated. . The steps provided hereinafter reflect a “two tier” strategy as discussed in (c) above. "/ X0 15 20 25 30 WO 98/10307 CA 02265875 1999-03-09 PCT/US97/15892 /* The following factor is analogous to the 2/3's factor discussed in (c) above. "/ cluster__ratio_factor < min{(si1eof(pt_est_bag) / min_clusters), |.0}; I" Now use “area" to obtain a new target MS location estimate and confidence based on archived verified loc sigs, but first determine whether “area" is too big to ignore the original target MS location estimate and confidence generated by the FOM . */ if (sizeof(area) > pt_max_area) then /* create a loc_hyp that is essentially a duplicate of the originally input “loc__hyp" except the confidence is lowered by “(I .0 - cluster_ratio_factor)". Note that the original “loc_hyp" will have its confidence computed below. "/ { new_loc_hyp < dup//iare(loc_hyp); // get a copy of the “loc__hyp" new_loc_hyp.image_area < NULL; // no image area for this new loc_hyp /* Now modify the confidence of “loc__hyp"; note, in the one embodiment, a system (i.e.. tunable) parameter may also be used as a coefficient in modifying the confidence here. ’/ new__loc_hyp.tonfidence < new_loc_hyp.confidente * (l.D - c|uster__ratio_factor) ; add_to__list(loc_hyp_list, new_loc_hyp); } /" Now compute the “image_area” field and a confidence that the target MS is in “image_area” ‘I image__cluster_set < gr-r_ rm‘//'ed_ /oc__s/gr_ c/r/.rter.t__/'0/(pt_est_bag); /* Note, this statement gets the verified location signature clusters for which the target MS point location estimates (for the first Order Model identified by “loc_hyp.f0lf_lD") in “pt_est_bag" are approximations. Note that the set of MS location point estimates represented in “pt__est_bag" is defined as a “c/rzrrerref hereinabove.*'/ image_area < ger_ area__ (onra/bi/rg(image_cluster_set); /* Note. in obtaining an area here that contains these verified location signature clusters, various embodiments are contemplated. In a first embodiment. a (minimal) convex hull containing these clusters may be provided here. In a second embodiment, a minimal covering of cells from the mesh for the FOM identified by “loc_hyp.F0lf_lD” may be used. ln a third embodiment. a minimal covering of mesh cells maybe used to cover the convex hull containing the clusters. It is assumed hereinbelovv that the first embodiment is used. Note, that this area is also denoted the “image r/urterretarea" as is described hereinabove. */ amt//_ to(loc__hyp.image_area, image_area); /" flake "image_area" the “image_area” field of "loc__hyp". */ 1"‘ In the following step, determine a confidence value for the target MS being in the area for “image_area”. */ confidence < confidence__adjuster(|oc_hyp.f0ll_lD, image_area, image_c|uster_set); /" In the following step, reduce the value of confidence if and only if the number of MS point location estimates in “pt_est_bag" is smaller than "min_clusters” */ I67 10 15 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 loc_hyp.confidence <--- confidence "" cluster_ratio_factor; llETUllN(loc_hyp_list); }suoor get_adiusted_loc_hyp_list__for confidence_adjuster(FOM_lD, image_area, image_cluster_set) I‘ This function returns a confidence value indicative of the target MS I40 being in the area for "image_area". Note that the steps herein are also provided in flowchart form in Figs. 27a and 27b. RETURNS: A confidence value. This is a value indicative of the target MS being located in the area represented by “image_area" (when it is assumed that for the related "loc_hyp," the "cluster set area" is the “|oc_hyp.pt_covering" and “loc_hyp.fOM__|D" is “F0lf_lD"); Introductory Information Related to the Function. “confidence_adiuster” This function (and functions called by this function) presuppose a framework or paradigm that requires some discussion as well as the defining of terms. Define the term "mapped cluster density" to be the number of the verified location signature clusters in an "image cluster set" per unit of area in the "image cluster set area". it is believed that the higher the “mapped cluster density”, the greater the confidence can be had that a target MS actually resides in the "image cluster set area” when an estimate for the target MS (by the present POM) is in the corresponding “the cluster set". Thus, the mapped cluster density becomes an important factor in determining a confidence value for an estimated area of a target MS such as, for example, the area represented by “image__area". However. the mapped cluster density value requires modification before it can be utilized in the confidence calculation. in particular. confidence values must be in the range [-l, l] and a mapped cluster density does not have this constraint. Thus, a “relativized mapped cluster density” for an estimated MS area is desired. wherein this relativized measurement is in the range [-I, + l], and in particular, for positive confidences in the range [0, l]. Accordingly, to alleviate this difficulty, for the TOM define the term “prediction mapped cluster density" as a mapped cluster density value. MED, for the POM and image cluster set area wherein: (i) MD is sufficiently high so that it correlates (at least at a predetermined likelihood threshold level) with the actual target MS location being in the “image cluster set area“ when a FOM target MS location estimate is in the corresponding “cluster set area”; That is. for a cluster set area (e.g., "loc_hyp.pt_covering") for the present TOM, if the image cluster set area: has a mapped cluster density greater than the “prediction mapped cluster density", then there is a high likelihood of the target MS being in the image cluster set area. l68 I0 20 25 30 CA 02265875 1999-03-09 W0 98,1030-, PCTIUS97/15892 It is believed that the prediction mapped cluster density will typically be dependent on one or more area types. In particular, it is assumed that for each area type, there is a likely range of prediction mapped cluster density values that is substantially uniform across the area type. Accordingly. as discussed in detail hereinbelow, to calculate a prediction mapped cluster density lor a particular area type, an estimate is made of the correlation between the mapped cluster densities of image areas (from cluster set areas) and the likelihood that if a verified MS location: (a) has a corresponding TOM MS estimate in the cluster set, and (b) is also in the particular area type, then the verified MS location is also in the image area. Thus, if an area is within a single area type, then such a “relativized mapped cluster density" measurement for the area may be obtained by dividing the mapped cluster density by the prediction mapped cluster density and taking the smaller of: the resulting ratio and l.0 as the value for the relativized mapped cluster density. In some (perhaps most) cases, however. an area (e.g., an image cluster set area) may have ponions in a numberof area types. Accordingly, a “composite prediction mapped cluster density" may be computed, wherein, a weighted sum is computed of the prediction mapped cluster densities for the portions of the area that is in each of the area types. That is, the weighting, for each of the single area type prediction mapped cluster densities, is the fraction of the total area that this area type is. Thus, a “relativized composite mapped cluster density" for the area here may also be computed by dividing the mapped cluster density by the composite prediction mapped cluster density and taking the smaller of: the resulting ratio and l.0 as the value for the relativized composite mapped cluster density. Accordingly, note that as such a relativized (composite) mapped cluster density for an image cluster set area increases/decreases, it is assumed that the confidence of the target MS being in the image cluster set area should increase/decrease, respectively. */ prediction_mapped_cluster_density < get_composite_prediction_mapped_cluster__i:|ensity__with_high_certainty (FOM_lD. image_area); /* The function invoked above provides a “composite prediction cluster density" (i.e., clusters per unit area) that is used in determining the confidence that the target MS is in “image_area". That is, the composite prediction mapped cluster density value provided here is: high enough so that lor a computed mapped cluster density greater than or equal to the composite prediction cluster density , and the target MS TOM estimate is in the “cluster set area", there is a high expectation that the actual target MS location is in the "image cluster set area” . */ max_area < ger_max_a/t-a_/ar_/14';/2_ re/mi/rt)(f0M_lD, image_area); /" Get an area size value wherein it is highly likely that for an area of size. “max_area". surrounding “image_area", the actual target MS is located therein. Note. that one skilled in the art will upon contemplation be able to derive various embodiments of this function, some embodiments being similar to the steps described for embodying the function, “get_composite_prediction_mapped_tluster_density_with_high__certainty" invoked above; i.e.. performing a Monte Carlo simulation. *'/ l69 CA 02265875 1999-03-09 wo 93119307 PCT/US97/15892 /* Given the above two values. a Qririreconfidence value for the area, “image__area", can be calculated based on empirical data. There are various embodiments that may be used to determine a confidence for the "image_area". In general, such a confidence should vary monotonically with (a) and (b) below; that is, the confidence should increase (decrease) with: 5 (a) an increase (decrease) in the size of the area. pair/a//ar/yif the area is deemed close or relevant to the location of the target MS; and (b) an increase (decrease) in the size of the image cluster set (i.e., the number of verified location signature clusters in the area that each have a location estimate, from the POM identified by “f0ll__lD". in the “cluster set" corresponding to the “image_cluster_set;" e.g., the “cluster set” being a “loc_hyp.pt_coveriog"). 10 As one skilled in the art will understand, there are many functions for providing conlidences that vary monotonically with (a) and(b) above. In particular. for the cluster set area being “loc_hyp.pt_covering", one might be inclined to use the (area) size of the image cluster area as the value for (a), and the (cardinality) size of the image cluster set as the value for (h). Then, the following term might be considered lor computing the confidence: (sizeof(image cluster set area) * (sizeof(image cluster set)) which, in the present context, is equal to 15 (sizeol(“image_area") "' (sizeof(“image_c|uster_set")). However, since confidences are intended to be in the range [-|.l], a normalization is also desirable for the values corresponding to (a) and (b). Accordingly, in one embodiment, instead of using the above values for (a) and (b). ratios are used. That is, assuming for a “relevant” area,A (e.g.. including an image cluster set area of “loc_hyp.pt_covering”) that there is a very high confidence that the target MS is in A, the following term may be used in place of the term. 20 sizeol(“image_area"), above: min { [sizeol(“image_area") /sizeol(A)]. l.0 }. [Ul|.l] Additionally, for the condition (b) above, a similar normalization maybe provided. Accordingly, to provide this normalization, note that the term. (sizeof(image_area) "* prediction_mapped_c|uster_density) [CAl.|.l] 25 is analogous to sizeof(A) in [CALI]. That is. the expression of [Clll.l.l] gives a threshold for the number of verified location signature clusters that are likely to be needed in order to have a high confidence or likelihood that the target MS is in the area represented by “image_area". Thus, the following term may be used for the condition (h): min {(sizeof(image_cluster_set)/ [(sizeof(image_area) ’-’ prediction__mapped_cluster_density], L0} 30 [CAL2] As an aside, note that sizeof(image_cluster_set) /[sizeof(image_area) * prediction_mapped_cluster. _density] is equivalent to [sizeof(image_cluster__set) /sizeof(image_area)] / (prediction_mapped _cluster_density) I70 10 15 20 25 CA 02265875 1999-03-09 wo 93,1030-; PCTlUS97ll5892 and this latter term may be interpreted as the ratio of: (i) the mapped cluster density for "image_area" to (ii) an approximation of a cluster density providing a high expectation that the target MS is contained in "image_area". Note that the product of [CALI] and [CAL2] provide the above desired characteristics for calculating the confidence. However. there is no guarantee that the range of resulting values from such products is consistent with the interpretation that has been placed on (positive) confidence values; e.g., that a confidence of near |.0 has a very high likelihood that the target MS is in the corresponding area. For example, it can be that this product rarely is greater than 0.8, even in the areas of highest confidence. Accordingly, a “tuning” function is contemplated which provides an additional factor for adjusting of the confidence. This factor is, for example, a function of the area types and the size of each area type in “image__area" . Moreover, such a tuning function may be dependent on a “tuning coefficient” per area type. fhus, one such tuning function may be: number of area types min(S [tq "* sizeof(area type; in “image_area") /sizeof ("image_area")], L0) where tci is a tuning coefficient (determined in background or off-line processing; e.g.. by a Genetic Algorithm or Monte Carlo simulation or regression) for the area type indexed by “i”. Note that it is within the scope of the present invention, that other tuning functions may also be used whose values may be dependent on. for example, Monte Carlo techniques or Genetic Algorithms. it is interesting to note that in the product of [CAM] and [CAL2]. the "image_area" size cancels out. This appears to conflict with the description above of a desirable confidence calculation. However, the resulting (typical) computed value: [CAD] is strongly dependent on “image_area" since “image__cluster_set" is derived from "image_area" and [sizeof(image_cluster_set)]/ [max_area * prediction_mapped__cluster_density] “prediction_mapped_cluster_density" also depends on "image_area". Accordingly, it can be said that the product [('Al.3] above for the confidence does not depend on “raw" area size. but rather depends on a "relevant" area for locating the target MS. An embodiment of the confidence computation follows: “I area_ratio < min((sizeof(image_area) / max_area), l.0); cluster__density_ratio < «- min( ((sizeof(image_cluster_set) / [sizeof(image__area) ’ (prediction_mapped_cluster_density)]), l.O ); tunab|e_constant < ger_ra/rfir/ence_ tr//ri/rg_ ca/r.rntrrr(image_area): // as discussed in the comment above confidence <--- (tunable_constant) * (area_ratio) * (cluster_density_ratio): //T his is in the range [0, l] l'lETllllN(confidence); |7l 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/U397/15892 } ENDOF confidence_adiuster get;__composite_prediction_mapped_cluster_density__with_high_certainty (FOM_|D, image_area); /* The present function determines a composite prediction mapped cluster density by determining a composite prediction mapped cluster density for the area represented by “image_area" and for the first Order Model identified by “FOM_lD". The steps herein are also provided in flowchart form in fig. 28. OUTPUT: composite_mapped_density This is a record for the composite prediction mapped cluster density. In particular. there are with two fields: (i) a "value" field giving an approximation to the prediction mapped cluster density for the first Order Model having id. f0M_lD; (ii) a “reliability” field giving an indication as to the reliability of the “value” field. The reliability field is in the range [0, I} with 0 indicating that the “value" field is worthless and the larger the value the more assurance can be put in “value" with maximal assurance indicated when "reliability" is l."/ I” Determine a fraction of the area of "image_area" comained in each area type (if there is only one. e.g.. dense urban or a particular transmission area type as discussed in the detailed description hereinabove, then there would be a fraction having a value of I for this area type and a value of zero [or all others). "‘/ composite_mapped__density < (J; // initialization for each area_type intersecting “image_area" do // “area_type" may be taken from a list of area types . { /* determine a weighting for "area_type" as a fraction of its area in “image_area" ’*/ intersection < /'/rtersecr(image_area. area_ /'ar(area_type)); weighting < sizeol(intersection) /sizeof(area_image); I‘* Now compute a prediction cluster density that highly correlates with predicting a location of the target MS for this area type. Then provide this cluster density as a factor of a weighted sum of the prediction cluster densities of each of the area types, wherein the weight for a particular area type's prediction cluster density is the fraction of the total area of “image_area” that is designated this particular area type. Note that the following function call does not utilize information regarding the location of “image_area". Accordingly, this function may access a precomputed table giving predication mapped cluster densities for (f0M__|l). area_type) pairs. However, in alternative embodiments of the present invention, the prediction mapped cluster densities may be computed specifically for the area of "image__area" intersect “area_type". */ I72 10 15 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/15892 prediction__mapped_density < get_prediction_mapped__cluster_density_for(f0M_lD. area_type); composite_mapped_density <--- composite_mapped_density + (weighting * prediction_mapped_density); } RETUllll(composite_mapped_density); } ENDOF get_composite__prediction_mapped__cluster__density_with__high__certainty get_prediction_mapped_c|uster_density_for(FOM__|D, area_type) /' The present function determines an approximation to a prediction mapped cluster density, 0, for an area type such that if an image cluster set area has a mapped cluster density > = D, then there is a high expectation that the target MS I40 is in the image cluster set area. Note that there are a number of embodiments that may be utilized for this function. The steps herein are also provided in flowchart form in figs. 292 through 29h. OUTPUT: prediction_mapped_cIuster_density This is a value giving an approximation to the prediction mapped cluster density for the first Order Model having identity, "f0M_lD", and for the area type represented by “area_type" */ introductory Information Related to the Function, “get_predication_mapped_cluster_density_for” It is important to note that the computation here for the prediction mapped cluster density may be more intense than some other computations but the cluster densities computed here need not be performed in real time target MS location processing. That is, the steps of this function may be performed only periodically (e.g., once a week), for each f0l‘l and each area type thereby pretomputing the output for this function. Accordingly. the values obtained here may be stored in a table that is accessed during real time target MS location processing. However, for simplicity. only the periodically performed steps are presented here. However, one skilled in the art will understand that with sufficiently fast computational devices. some related variations of this function may be performed in real-time. In particular, instead of supplying area type as an input to this function, a particular area. A, may be provided such as the image area for a cluster set area, or, the portion of such an image area in a particular area type. Accordingly, wherever “area_type” is used in a statement of the embodiment of this function below, a comparable statement with “A” can be provided. { mesh <--- ger_mes/I_/m(f0l‘l_lD); /* get the mesh for this first Order Model; preferably each cell of “mesh" is substantially in a single area type. ‘l H} IO 15 20 25 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/ 15892 max_nbr_simulations < --- get_ flo/ire_ [ar/o_:/'mu/ati'on_ /lb/(T0ll_lD, area_type); /* This function outputs a value of the maximum number of simulations to perform lor estimating the prediction mapped cluster density. Note that the output here may always be the same value such as I00. ‘*/ nbr_simulations_performed < 0; // initialization while (nbr_simulations_performed < = max_nbr_simulations) do // determine a value for the "average mapped cluster density" and a likelihood of this value being predictive of an MS location. "/ representative_cell__cluster_set < gt-r_ repre.renrarive_ re/L (/aster.r.r_ for(area_type, mesh); /* Note, each activation of this function should provide a different set of cell clusters from a covering from “mesh” of an (sub)area of type, “area_type". There should ideally be at least enough substantially different sets of representative cell clusters so that there is a distinct sets of cell clusters lor each simulation number, 3. Further note that, in one embodiment. each of the “representative cell cluster sets" (as used here) may include at least a determined proportion of the number ol cells distributed over the area type. l‘|oreover,_each cell cluster (within a representative cell cluster set) satisfies the following: A. The cell cluster is a minimal covering (from “mesh") of a non-empty area, A, of type “area_type" ("A" being referred to herein as the associated area for the cell cluster); B. The cells of the cluster form a connected area; note this is not absolutely necessary; however. it is preferred that the associated area “A" of “area_type" covered by the cell cluster have a "small" boundary with other area types since the “image_areas” computed below will be less likely to include large areas of other area types than “area_type;" C. There is at least a predetermined minimal number (> = l) of verified location signature clusters lrom the location signature data base whose locations are in the associated area “A”. D. The cell cluster has no cell in common with any other cell cluster output as an entry in “representative_ce|l_cluster_set" . */ if (representative_cell_cluster__set is NULL) then /* another representative collection of cell clusters could not be found; so cease further simulation processing here, calculate return values and return */ break; // iump out of “simulation loop" else I‘ there is another representative collection of cell clusters to use as a simulation */ { for each cell cluster. C, in “representative_cell_clusters" do /"‘ determine an approximation to the predictiveness of the mappings between: (a) cluster set areas wherein each cluster set area is an area around a (F0l1_lD) TOM estimate that has its corresponding verilied location in "C." and (b) the corresponding image areas for these cluster set areas. Note, the location signature data base includes at least one (and preferably more) I74 10 15 20 30 CA 02265875 1999-03-09 W0 98/10307 PCT/US97/ 15892 location signature clusters having verified locations in each cell cluster C as per the comment at (C) above. “’/ { random_list < /amiam/y_ se/ea_ veri/7ed_ I/5_ /a(s_ i/;(C); /"‘ select one or more verified MS locations from C. "I mapped_density_sum < 0; // initialization for each verified location, “rand_verif_|oc", in “random_list” do /* Let X denote the HS I40 estimate by the present FOM of the verified location signature cluster of “rand_veril_loc": let CS(X) denote the cluster set obtained from the cluster set area (i.e., pt_area) surrounding X; this loop determines whether the associated image area for the set CS(X) - X, (i.e., the image area for (S00 without “rand_verif_|oc") includes “rand_verif_loc"; i.e.. try to predict the location area of “rand_verif_loc". */ { loc__est < gpt_ /or_ e:I_ /ar(rand_verif_|oc, f0H_lD); /"‘ get the FOM MS location estimate for an MS actually located at “rand_verif__loc". "‘/ c|uster__set <--- ger_ /o(_ estr_ surround/'ng(loc_est, mesh); /* expand about “loc_est" until a minimal number of other location estimates from this FOM are obtained that are different from “|oc_est". or until a maximum area is reached. Note, “c|uster__set" could be empty. but hopefully not. Also note that in one embodiment of the function here. the following functions not may be invoked: “get_min__area_surrounding, get_max_area_surrounding" and “get_min_nbr_of_c|usters" (as in "get_adiusted_|oc_hyp_list_for", the second function of Appendix D): */ image_set <--- ger__ /'/»age_ 0/(cluster_set); /"‘ “image_set" could be empty, but hopefully not */ image__area <--- get_ image_area(image_est); I“ get convex hull of "image_set". Note, “image_area" could he an empty area, but hopefully not. “‘/ if (rand_vcrif_loc is in image_area) then 1"‘ this is one indication that the mapped cluster density: (sizeof[image_set]fimage_area) is sufficiently high to be predictive */ predictions <.-- predictions + I; if (image_set is not empty) then { density < sizeof(image_set) /sizeof(image_area); /* Get an approximation to the mapped cluster density that results from "image_set" and “image_area." Note, that there is no guarantee that “image_area" is entirely within the area type of “area_type." Also note, it is assumed that as this mapped cluster density increases, it is more likely that “rndm_verif_luc" is in “image_area". */ I75 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97/15892 mapped_density_sum <--- mapped_density_sum + density; } } /" end loop for predicting location of a random MS verified location in cell cluster C ‘I total_possib|e_predictions < sizeol(random_list); // One prediction per element on list. /"' Now get ave/age mapped density for the cell cluster C. */ avg__rnapped‘density[C] < mapped__density_sum /total_possible_predictions; /* Now get the prediction probability for the cell cluster C. '/ prediction__probability [C] < predictions / total_possible_predictions; } /' end loop over cell clusters C in “representative_cel|_clusters" */ nbr_simulations_perlormed < nbr_simulations_perlormed + l; } // end else /* It would be nice to use the set of pairs (avg_mapped_density [C], prediction_probability[C]) lor extrapolating a mapped density value for the area type that gives a very high prediction probability. However, due to the potentially small number of verilied MS locations in many cells (and cell clusters). the prediction probabilities may provide a very small number of distinct values such as: 0, V2, and l. Thus. by averaging these pairs over the cell clusters of “representative_cell_clusters", the coarseness of the prediction probabilities may be accounted lor. */ avg_mapped_c|uster_density[nbr_simulations_perlormed] <--- avg_ 0/_ ce//_ mapper/_ dens/fies(avg_mapped_density); avg__prediction_probability[nbr_simulations_per1ormed] <--- avg_a/_ re//_ pred/ct/on_p/abab/'//'r1'e.r(prediction_probability); } /* end simulation loop “/ /* Now determine a measure as to how reliable the simulation was. Note that "reliability" computed in the next statement is in the range [0, I}. '/ reliability < nbr_simulations_perlormed / max_nbr~simulations; il (reliability < system_defined_epsilon) then /* simulation too unreliable; so use a default high value lor "prediction_mapped_cluster_tlensity" '/ prediction_mapped_cluster_density < get_delault_high_density_value_lor(area__type); else /*‘ simulation appears to be sulliciently reliable to use the entries oi “avg__mapped_cluster_density" and “avg_prediction_probability" "/ /* A more easily discernible pattern between mapped cluster density and prediction probability maybe provided by the set of pairs: S = {(avg_mapped_c|uster_density [j], avg_prediction_probability[j])}. so that a mapped cluster density value having a high prediction probability (e.g.. 0.95) may be extrapolated in the next statement. However, if it is I76 CA 02265875 1999-03-09 wo 93/10307 PCT/US97/15892 determined (in the function) that the set S does not extrapolate well (due to lor example all ordered pairs of S being clustered in a relatively small region), then a “NULL" value is returned. */ prediction_mapped_cluster_density < mapper{_ r/I/ster_ dc-/rsiry_ ext/apa/aria/r(avg_mapped_cluster_density, avg_prediction_probability. 0.95); 5 if ( (prediction_mapped_c|uster_density = = NULL) then /’ set this value to a default “high” value for the present area type"/ prediction_mapped_cluster__density< get_delault__high_density_value_lor(area_type); else // So both “prediction_mapped_t|uster_density" and it's reliability are minimally OK. /* Now take the "reliability" of the “prediction_mapped_cluster_density" into account. Accordingly. as the to reliability r/ecrearerthen the prediction mapped cluster density should be //rmareai However. there is a system defined upper limit on the value to which the prediction mapped cluster density may be increased. The next statement is one embodiment that takes all this into account. Of course otherembodiments are also possible. */ prediction_mapped_cluster_density< 15 min {(prediction_mapped_cluster_density / reliability), get_delault_high_density_value_for(area_type)}; } // end else for simulation appearing reliable llETl.lllN(prediction_rnapped_cluster_density); }EllDOF get_prediction__mapped_c|uster_density_for I77 U: 20 25 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/U S971 15892 A Second Embodiment of the Context Adjuster: Note that in this second embodiment of the Context Adjuster, it uses various heuristics to increment/decrement the confidence value of the location hypotheses coming from the first Order Models. These heuristics are implemented using fuzzy mathematics. wherein linguistic, fuzzy "if-then" rules embody the heuristics. That is, each fuzzy rule includes terms in both the "if" and the "then" portions that are substantially described using natural language — like terms to denote various parameter value classifications related to, but not equivalent to, probability density functions Further note that the Context Adjuster and/or the f0M's may be calibrated using the location information from l.BSs (i.e., fixed location BS transceivers), via the Location Base Station Model since such LBS's have well known and accurate predetermined locations. Regarding the heuristics of the present embodiment of the context adjuster, the following is an example of a fuzzy rule that might appear in this embodiment of the Context Adjuster: If <the season is Fall > then < the confidence level of Distance Model is increased by S%>. In the above sample mle, "Distance Model" denotes a first Order Model utilized by the present invention. To apply this sample mle, the fuzzy system needs a concrete definition of the term "Fall." in traditional expert systems, the term fall would be described by a particular set of months, for example, September through November, in which traditional set theory is applied. In traditional set theory, an entity, in this case a date, is either in a set or it is not in a set, e.g. its degree of membership in a set is either 0, indicating that the entity is not in a particular set, or 1. indicating that the entity is in the set. However, the traditional set theory employed in expert systems does not lend itself well to entities that fall on set boundaries. for example, a traditional expert system could take dramatically different actions for a date of August 3! than it could for a date of September I because August 3l might belong to the set "Summer" while the date September I might belong to the set "fall." This is not a desirable behavior since it is extremely difficult if not impossible to determine such lines of demarcation so accurately. However, fuzzy mathematics allows for the possibility of an entity belonging to multiple sets with varying degrees of confidence ranging fmm a miniirium value of 0 (indicating that the confidence the entity belongs to the particular set is minimum) to l (indicating that the confidence the entity belongs to the particular set is maximum). The "fuzzy boundaries" between the various sets are described by fuzzy membership functions which provide a membership function value for each value on the entire range of a variable. As a consequence of allowing entities to belong to multiple sets simultarieously, the fuzzy rule base might have more than one rule that is applicable for any situation. Thus, the actions prescribed by the individual rules are averaged via a weighting scheme where each mle is implemented in proportion to its minimum confidence. for further information regarding such fuzzy heuristics, the following references are incorporated herein by reference: (McNeil and Freiberger. I993; Cox, I994; Klir and folger, I999; Zimmerman, l99l). Thus. the mles defined in the fuzzy mle base in conjunction with the membership functions allow the heuristics for adjusting confidence values to be represented in a liirguistic form more readily understood by humans than many other heuristic representations and thereby making it easierto maintain and modify the rules. The fuzzy rule base with its membership functions can be thought of as an extension I78 10 20 25 30 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97I15892 to a traditional expert system. Thus, since traditional expert systems are subsets of fuzzy systems, an alternative to a fuzzy rule base is a traditional expert system, and it is implicit that anywhere in the description of the current invention that a fuzzy rule base can be replaced with an expert system. Also, these heuristics may evolve over time by employing adaptive mechanisms including, but not limited to. genetic algorithms to adjust or tune various system values in accordance with past experiences and past perfomrance of the Context Adjuster for increasing the accuracy of the adjustments ruade to location hypothesis confidence values For example, in the sample rule presented above: If <the season is Fall> then <the confidence level of Distance Model is increased by S%> an adaptive mechanism or optimization routine can be used to adjust the percent increase in the confidence level of the Distance ModeL For example, by accessing the MS Status Repository, a genetic algorithm is capable of adjusting the fuzzy rules and membership functions such that the location hypotheses are consistent with a majority of the verified MS locations. In this way, the Context Adjuster is able to employ a genetic algorithm to improve its perfomrance over time. for further infomration regarding such adaptive mechanisms, the following references are incorporated herein by reference: (Goldberg, I989; Holland, I975). for further information regarding the tuning of fuzzy systems using such adaptive mechanisms, the following references are incorporated herein by reference: (llarr, l99la, l99lb). in one embodiment, the Context Adjuster alters the confidence values of location hypotheses according to one or more of the following envimnmental factors: (I) the type of region (e.g., dense urban. urban, rural, etc.), (2) the month of the year, (3) the time of day, and (4) the operational status of base stations (e.g., on-line or off-line), as well as otherenvironmental factors that may substantially impact the confidence placed in a location hypothesis. Note that in this embodiment. each envimnmental factor has an associated set of linguistic heuristics and associated membership functions that prescribe changes to be made to the confidence values of the input location hypotheses The context adjuster begins by receiving location hypotheses and associated confidence levels from the first Order Models. The Context Adjuster takes this information and improves and refines it based on environmental information using the modules described below. B.l COA Calculation Module As mentioned above each location hypothesis provides an approximation to the MS position in the form of a geometric shape and an associated confidence value. a. The COA calculation module detennines a center of area (COA) for each of the geometric shapes, if such a COA is not already provided in a location hypothesis The COA Calculation Module receives the following infonnation from each first Order Model: (I) a geometrical shape and (2) an associated confidence value, a. The COA calculation is made using traditional geometric computations (numerical algorithms are readily available). Thus, following this step, each location hypothesis includes a COA as a single point that is assumed to represent the most hkely approximation of the location of the MS. The COA Calculation Module passes the following information to the fuzzifrcation module: (1) a geometrical shape associated with each first order model I224, (2) an associated confidence value, and (3) an associated COA. 3.2 Fuzzification Module 10 20 30 CA 02265875 1999-03-09 W0 98/ 10307 PCT/U S97/ 15892 A fuzzification module receives the following information from the EDA Calculation Module: (l) a geometrical shape associated with each first Order Model, (2) an associated confidence value, and (3) an associated COA. The fuzzification Module uses this information to compute a membership function value (u) for each of the M location hypotheses received from the con calculation module (where the individual models are identified with an i index) for each of the N environmental factors (identified with a j index). In addition to the information received fmm the COA Calculation Module, the fuzzilication Module receives infomtation from the Location Center Supervisor. The fuzzification module uses current environmental information such as the current time of day, month of year, and information about the base stations on-fine for communicating with the MS associated with a location hypothesis currently being processed (this information may include, but is not limited to, the number of base stations of a given type, e.g., location base stations, and regular base stations, that have a previous history of being detected in an area about the COA fora location hypothesis). The base station coverage infomiation is used to compute a percentage of base stations reporting for each location hypothesis. The fuuification is achieved in the traditional fashion using fuzzy membership functions for each environrriental factor as, for example, is described in the following references incorporated herein by reference: (Mclleil and freiberger, T993: Cox, I994; lllir and folger, I999; Zimmemian, l99l). Using the geographical area types for illustration purposes here, the following procedure might be used in the fuzzification Module. Each value of COA for a location hypothesis is used to compute membership function values ()u) for each of five types of areas: (I) dense urban (pm), (2) urban (an), (3) suburban (us), (4) rural plain W"). and (5) rural mountains (pm). These membership function values provide the mechanism for representing degrees of membership in the area types, these area types being determined from an area map that has been sectioned off. In accordance with fuzzy theory, there may be geographical locations that include, for example, both dense urban and urban areas; dense urban and rural plane areas; dense urban, urban, and rural plane areas, etc. Thus for a particular MS location area estimate (described by a COA), it may be both dense urban and urban at the same time. The resolution ofany apparent conflict in applicable rules is later resolved in the Defuzzification Module using the fuzzy membership function values (U) computed in the fuzzification Module. Any particular value of a COA can land in more than one area type. for example, the COA may be in both dense urban and urban. further, in some cases a location hypothesis for a particular first Order Model i may have membership functions pwi, ;1.,i,;J;,;J,p', and pkg’ wherein they all potentially have non-zero values. Additionally, each geographical area is contoured. Note that the membership function contours allow for one distinct value of membership function to be determined for each cox location 6.8., there will be distinct Values of ,u,,,', ii,‘,ii,‘, ii,,", and /Jmi for any single COA value associated with a particular model i). forexample, the (DA would have a dense urban membership function value,uwi, equal to 0.5. Similar contours would be used to compute values ofyui, ,L1,i,t1Rp', and um‘. Thus, for each COA, there now exists an array or series of membership function values; there are If membership function values ( = number of descriptive terms forthe specified environmental factor) for each of M first Order Models. Each COA calculation has associated with it a definitive value for um‘, ,u,,i, /.13i,t1,,‘, and um‘. Taken collectively, the M location hypotheses with membership function values for the l( descriptive terms for the particularenvironmental factor results in a membership function value matrix. Additionally, similar membership function values are computed for each of the N environmental factors, thereby resulting in a corresponding membership function value matrix foreach of the N environmental factors. I80 10 20 25 30 CA 02265875 1999-03-09 wo 98IlO307 PCT/Us97/15392 The fuzzifrcation Module passes the N membership function value matrices described above to the Rule Base Module along with all of the information it originally received from the COA Calculation Module. 3.3 Rule Base Module The llule Base Module receives from the fuzzification Module the following informatiom (l) a geometrical shape associated with each first Order Model, (2) an associated confidence value, (3) an associated COA, and (4) N membership function value matrices. The Rule Base Module uses this information in a manner consistent with typical fuzzy rule bases to determine a set of active or applicable rules. Sample rules were provided in the general discussion of the Context Adjuster. Additionally, references have been supplied that describe the necessary computations. Suffice it to say that the llule Base Modules employ the information provided by the Fuzzification Module to compute confidence value adjustments for each of the m location hypotheses. Associated with each confidence value adjustment is a minimum membership function value contained in the membership function matrices computed in the fuzzification Module. for each location hypothesis, a simple inference engine driving the rule base queries the perfomiance database to detennine how well the location hypotheses for the first Order Model providing the current location hypothesis has performed in the past (for a geographic area surrounding the MS location estimate of the currem location hypothesis) under the present environmental conditions. For example. the performance database is consulted to determine how well this particular first Order Model has perfomied in the past in locating an MS forthe given time of day, month of year, and area type. Note that the perfomiance value is a value between 0 and I wherein a value of 0 indicates that the model is a poor performer, while a value of l indicates that the model is always (or substantially always) accurate in determining an MS location under the conditions (and in the area) being considered. These performance values are used to compute values that are attached to the current confidence of the current location hypothesis; i.e, these performance values serve as the "then" sides of the fuzzy rules; the first Order Models that have been effective in the past have their confidence levels incremented by large amounts while first Order Models that have been ineffective in the past have their confidence levels incremented by small amounts. This inlomtation is received from the Perfomiance Database in the form of an envimnmental factor, a first Order Model number, and a perfomiance value. Accordingly, an intemiediate value for the adjustment of the confidence value for the current location liypothesis is computed foreach environrriental condition (used by Context Adjuster) based on the performance value retrieved from the Performance Database. Each of these intennediate adjustment values are computed according to the following equation which is applicable to area information: . ‘ i MX adiustmentf = Da = perfonnance_value, * Dam)», where a is the confidence value of a particular location hypothesis, performance_value is the value obtained from the Perfomiance Database, Daimonm is a system parameter that accounts for how important the information is being considered by the context adjuster. furthermore, this parameter is initially provided by an operator in, for example, a system start-up configuration and a reasonable value for this pammeter is believed to be in the range 0.05 to 0.1, the subscriptj represents a particular environmental factor, and the superscript i represents a particular first Order Model. However, it is an important aspect of the present invemion that this value can be repeatedly altered by an adaptive mechanism such as a genetic algorithm for improving the MS location accuracy of the present invention In this way, and because the rules are l8l 10 20 25 CA 02265875 1999-03-09 W0 98/ 10307 PCT/U S97] 15892 "written" using current performance information asistored in the Performance Database, the Rule Module is dynamic and becomes more accurate with time. The Rule Base Module passes the matrix of adjustments to the Deluzzification Module along with the membership function value matrices received from the Fuzzilication Module. 3.6 Deiuzzification Module The Defuzziliation Module receives the matrix of adjustments and the membership function value matrices fmm the Rule Base Module. The final adjustment to the first Order Model confidence values as computed by the Context Adjuster is computed according to: Z/1',(k)Aa', Aaiflf) = I—-l'7,—"—‘— Zr!’/k) I“ such as, but not limited to. time of day, month of year, and base station coverage, there are a number of system start-up configuration parameters that can be adjusted in attempts to improve system performance. These adjustments are. in effect. adjustments computed depending on the previous performance values of each model under similar conditions as being currently considered. These adjustments are summed and forwarded to the blackboard. Thus, the Context Adjuster passes the following information to the blackboard: adjustments in confidence values for each of the first Order Models based on environmental factors and COA values associated with each location hypothesis. Summary The Context Adjuster uses environmental factor information and past perfomtance infonnation for each of i First Order Models to compute adjustments to the current confidence values. It retrieves infonnation lmm the first Order Models, interacts with the Supervisor and the Performance Database, and computes adjustments to the confidence values. Further. the Context Adjuster employs a genetic algorithm to improve the accuracy of its mlculations. The algorithm for the Context Adjuster is included in algorithm BE.B below: Algorithm BE.B: Pseudocode forthe Context Adjuster. Context_Adjuster (geometries, alpha) /' This program implements the Context Adjuster. It receives from the first Order Models geometric areas contained in a data structure called geometries. and associated confidence values contained in an array called alpha. The program used environmental information to compute improved numerical values of the confidence values. It places the improved values in the array called alpha, destroying the previous values in the process. */ // pseudo code for the Context Adjuster // assume input from each of i models includes a // geographical area described by a numberof points 10 15 20 25 30 CA 02265875 1999-03-09 W0 98,10307 PCT/US97/15892 // and a confidence value alpha(i). alpha is such // that if it is 0.0 then the model is absolutely // sure that the MS is not in the prescribed area; // if it is L0 then the model is absolutely // sure that the MS is in the prescribed area. // calculate the center of area for each of the i model areas fori = l to number_of_models calculate center of area // termed coa(r) from here on out // extract information from the ”outside world” or the environment find time_of_day find month_ol_year find number__of_BS__available find number_of_BS_reporting // calculate percent; coverage of base stations percent_coverage = l00.0 * (number_of_BS__reporting / number_of_BS_available) // use these /' = 4 environmental actors to compute ad/'ustments to the iconfidence values //associated with the imodels - alpha(i) fori = I to number_ol_models // loop on the numberof models for] = I to number_env_lactors // loop on the number of environmental factors fork = l to number_of_fuzzy_classes // loop on the numberol classes // used for each of the environmental // factors // calculate mu values based on membership function definitions calculate mu(i,j,k) values //go to the performance database and extract current performance information for each of the i //models in the Ir hazy classes, for the j environmental factors fetch perfomrance(i,j,lc) // calculate the actual values tor the right hand sides of the hazy rules delta_alpha(i,j,k) = performance(i.j,k) * delta_alpha_max(j) //delta_alpha_max(j) is a maximum amount each environmental // factor can alter the confidence value; it is eventually l83 20 CA 02265875 1999-03-09 W0 98/ 10307 // determined by a genetic algorithm //compute a weighted average; this is traditional frmy mathematics delta_alpha(i,j,k) = sum[mu(i,j,l<) * delta__alpha(i,j,k) / sum [mu(i,j,k)] end loop on k // number ol fuzzy classes //compute final deIta_aIpha values delta_alpha(i) = sum[de|ta_alpha(i,j)] end looponj // numberol envimnmental lactors alpha(i) + = delta_alpha(i) end loop on i // number of models // send alpha values to hlac/(board send delta_alpha(i) to blackboard //see if it is time to interact with a genetic algorithm if (in _progress) then continue to calculate alpha adjustments else call the genetic algorithm to adjust alpha_max parameters and mu functions l84 PCT /U S97! 15892 5 20 30 CA 02265875 1999-03-09 WO 98110307 PCT/US97/ 15892 APPENDIX E: Historical Data Confidence Adjuster Program I-listorical_data__confidence__adjuster(loc_hyp) /* This function adjusts the confidence of location hypothesis. “loc_hyp", according to how well its location signature cluster fits with verified location signature clusters in the location signature data base. */ { mesh < get_ /rm/7_ /o/(loc_hyp.f0M_|D); // each TOM has a mesh of the Location Center service area covering < —— ge{_ me:/1__ raven"/rg_ o{_ /‘/J'_ err/i7rare_ /0/(loc_hyp); /’ get the cells of “mesh” that minimally cover the most pertinent target MS estimate in “loc_hyp”. '/ total _per_unit_error < 0; // initialization for each cell, C, of “covering” do /“* determine an error measurement between the location signature cluster of “loc_hyp" and the verified location signature clusters in the cell */ centroid <-- ger_ re/It/o/i1(C); error_obj < Dl!_Loc_Sig_Error__Fit(centroid, C, loc_hyp.loc_sig_cluster. “USE All. LOC SlGS Ill DB”); /* The above function call computes an error object, “error__obj", providing a measure of how similar the location signature cluster for “loc__hyp" is with the verified location signature clusters in the location signature data base, wherein the verified location signature clusters are in the area represented by the cell, C. See APPENDIX C lor details of this function. ‘I total_per_unit_error < total_per_unit_error + [error_obj.error * error_obj.confidence /sizeol(C)]; /" The above statement computes an “error per unit of cell area” term as: [error_obj.error * error_obj.conlidence / sizeof(C)], wherein the error is the term: error_obj.error * error_obj.conlidence. Subsequently, this error per unit of cell area tenn accumulated in “tota|_relative_error” '/ } avg_per_unit_error < -- total _per_unit_error / nbr__cells_in(mesh); /* Now get a tunable constant, “tunable_constant", that has been determined by the Adaptation Engine I382 (shown in Figs. 5, 6 and 8), wherein “tunable_constant" may have been adapted to environmental characteristics. "*/ tunable_constant < ger_ rrmeab/e_ amsra/rr_ fa/(“Historical_l.ocation_lieasoner". |oc_hyp); /* Now decrement the confidence value of “|oc_hyp” by an error amount that is scaled by “tunable_constant" */ I85 CA 02265875 1999-03-09 wo 93/10307 PCTIUS97I1S892 loc__hyp.confidence < -- |oc_hyp.confidence - [avg_per_unit_error * sizeof(covering) * tunable_constant]; REIURN(|oc_hyp); }ENDOf Historica|_data__confidence_adjuster I86

Claims (81)

1. A method for estimating, for each mobile station M of a plurality of mobile stations a location of M using wireless signal measurements obtained from communications with the mobile station M, wherein there are terrestrial communications stations for communicating with the mobile station M, and each of the communication stations has one or more of a transmitter and a receiver for wirelessly communicating with the mobile station M, comprising:
receiving a plurality of requests, each request for locating a corresponding one of the mobile stations at one or more locations;
wherein for each of the requests, the step of receiving occurs at a site that is independent of substantially all movements of the corresponding mobile station for the request;
for a first collection of the plurality of requests for locating a related plurality of the mobile stations, each request (R1) of the first collection of one or more of the plurality of requests, results in the performing of a step of first contacting a first of a plurality of location estimators for generating and requesting an instance of first location information for estimating a first location of the corresponding mobile station of the related plurality for the request R1, wherein the first location information is substantially dependent upon a result from an activation of an occurrence of a first location technique of a plurality of location techniques identified at (T) below;
for each request (R2) of a second collection of one or more of the plurality of requests, R2 being the same as a request of the first collection or different from every request of the first collection, performing a step of second contacting a second of said plurality of location estimators for generating and requesting an occurrence of second location information for estimating a second location of the corresponding mobile station for the request R2, wherein the second location information is substantially dependent upon a result from an activation of an occurrence of a second location technique of said plurality location techniques identified at (T) below;
wherein the second location technique is different from the first location technique;
(T) the following location techniques (T1) through (T4):

(T1) a location technique for determining locations of one or more of the mobile stations, wherein for the mobile station M, and for at least a corresponding one of the communication stations CS that is responsive to transmissions received from the mobile station M, at least one of (a) and (b) following is determined:
(a) data indicative of a locus of locations of the mobile station M from the communication station CS, wherein the locus is determined at a site different from the M, said locus dependent upon measurements of at least one of a signal strength and a time delay of signals transmitted between the mobile station M and the communication station CS, and (b) a wireless signal angle of arrival indicative of an angular orientation about the communication station CS of a direction of wireless transmissions to CS from M, wherein the angular orientation is determined using the wireless signal angle of arrival;
(T2) a location technique for determining locations of one or more of the mobile stations, wherein for the mobile station M, the present location technique T2 includes a signal processing technique for estimating a location of the mobile station M using timing data obtained from wireless signals received at the mobile station M from one or more non-terrestrial transmitting stations, which are above and not supported on the Earth's surface, wherein said timing data includes data indicative of one of: (a) a time differential between signal delay times obtained at the M from the wireless signals transmitted by two of the non-terrestrial transmitting stations, and (b) a signal time delay of the wireless signals transmitted to the M by at least one of the non-terrestrial transmitting stations;
wherein said location technique T2 locates the mobile station M by determining M
location related information indicative of one or more offsets of the mobile station M from one or more of the non-terrestrial transmitting stations;

(T3) a location technique for determining locations of one or more of the mobile stations, wherein for the mobile station M, the present location technique T3 includes a step of accessing a collection of associations, wherein for each location L a of a plurality of geographical locations, there is an association of the collection that associates (a1) and (a2) following:
(a1) a representation of the geographical location L a, and (a2) for the geographical location L a, corresponding information indicative of one or more characteristics of wireless signal transmissions transmitted between one of the mobile stations (M a) and at least one of the communication stations, wherein the transmissions are from approximately the geographical location L
a, the mobile station M a being different from M; and wherein location technique T3 additionally performs one of: a step of comparing (b1) and (b2) following, and, a step of determining a similarity in a pattern of wireless signal characteristics between (b1) and (b2) following:
(b1) one or more wireless signal characteristics determined from wireless signals communicated between the mobile station M and the communication stations, and (b2) the information of (a2) for one or more of the associations;
(T4) a location technique for determining locations of one or more of the mobile stations, wherein for the mobile station M, the present location technique includes an adaptive location technique for generating a geographical location estimate for the mobile station M, wherein the adaptive technique adapts its generated geographical location estimates according to changes in an archive of data collections wherein for each of a plurality of geographical locations, there is one of said data collections having (a) and (b) following:
(a) a representation of the geographical location, and (b) a set of said wireless signal data obtained using transmissions between one of said mobile stations, and the communication stations, wherein the one mobile station transmits from approximately the geographical location of (a), first obtaining from the first location estimator, one or more instances of the first location information corresponding to one or more requests of the first collection of requests, each instance including one of (c1) and (c2) following: (c1) a first indication of a location for a first mobile station (M1) of the mobile stations, and (c2) a second indication of a location for a second mobile station (M2) of the mobile stations; and second obtaining from the second location estimator, one or more occurrences of the second location information corresponding to the second collection of requests, each occurrence including one of (d1) and (d2) following: (d1) a third indication of a location for the first mobile station M1, and (d2) a fourth indication of a location for the second mobile station M2, outputting a corresponding resulting location estimate of each of the instances M1 and M2 to a corresponding destination, wherein the corresponding resulting location estimate for the instance M1 is obtained using one or more of the first and third indications, and the corresponding resulting location estimate for the instance M2 is obtained using one or more of the second and fourth indications, and wherein the corresponding destination for the instance M1 corresponds to one of the requests for locating the instance M1, and the corresponding destination for the instance M2 corresponds to one of the requests for locating the instance M2.
2. The method of Claim 1, wherein for determining one of the instances of the first location information for the mobile station M1, a further step of determining at least the first location estimator to contact in said first step of contacting by identifying input data appropriate for the first location estimator to determine the one instance of the first location information.
3. The method of Claim 1 further including a step of providing said resulting location estimate of M1 for presentation on a visual display, wherein said display presents information related to a corresponding accuracy or reliability of said resulting location information of M1.
4. The method of Claim 1 further including a step of providing said resulting location estimate of M1 for presentation on one or more graphical displays, wherein a map is concurrently displayed in at least one of said displays.
5. The method of Claim 1, further including a step of providing said resulting location estimate of M1 for presentation on one or more graphical displays, wherein the presentation is determined according to an expected accuracy of said resulting location information.
6. The method of Claim 1, wherein at least one of the instances of the first location information is for locating M1, and at least one of the occurrence of the second location information is for locating M1, and each of the at least one instance and the at least one occurrence are for the first mobile station M1 being in substantially a same location at a substantially same time.
7. The method of Claim 1, wherein at least one of the instances of the first location information is for locating M1, and at least one of the occurrence of the second location information is for locating M1, and wherein the at least one instance is for the first mobile station M1 being at a first location, and the at least one occurrence is for the first mobile station M1 being at a substantially different location.
8. The method of Claim 1, wherein said first and second mobile stations are for a same one of the mobile stations, M0 and further including a step of determining the corresponding resulting location estimate of the mobile station M0 by accessing at least one instance of the first location information, and at least one occurrence of the second location information.
9. The method of Claim 1, wherein said step of receiving includes receiving said first and second requests via a predetermined same access node of a communications network.
10. The method of Claim 1, wherein each of the mobile stations M1 and M2 are in two way wireless communication with a commercial mobile radio service provider via at least one base station of the commercial mobile radio service provider.
11. The method of any of the Claims 1 through 10, wherein when at least one of the instances of the first location information is for the first instance of M1 and at least one of the occurrences of the second location information is for the first instance of M1 each of the at least one of the instance of the first location information for the first instance of M1, and the at least one of the occurrence of the second location information for the first instance of M1 is substantially unaffected by the other.
12. The method of any one of the Claims 1 through 11, wherein the first location estimator activates an occurrence of the location technique T1, and the occurrence of the location technique T1 includes a step of determining a time difference of arrival between: (a) wireless signals from the mobile station M to the communication station CS, and (b) wireless signals from the mobile station M to another of the communication stations.
13. The method of any one of the Claims 1 through 11, wherein the first location estimator activates an occurrence of the location technique T1, and the occurrence of the location technique T1 includes a step of determining an intersection of the data indicative of the locus of locations with additional data indicative of another locus of locations of the mobile station M from a different one of the communication stations.
14. The method of any one of the Claims 1 through 13, wherein: (a) the first mobile station M1 is land borne, (b) the communication stations and the first mobile station M1 communicate using one of: a wirelesscommunication protocol for a commercial mobile radio provider, and (c) the step of outputting includes transmitting at least one of the corresponding resulting location estimates for M1 and M2 via one of a public switched telephone network and the Internet.
15. The method of any one of the Claims 1 through 14, wherein (a) and (b) following:
(a) for a mobile station (M1) of the mobile stations, an instance (ii) of the first location information for locating the mobile station M1 is obtained from the first location estimator, wherein one of: (i) the first location estimator does not include an activation of the occurrence of the second location technique by the second location estimator, and (ii) the instance 11; is not substantially affected by any result of the activation of the occurrence of the second location technique by the second location estimator, and (b) for a mobile station (Mk) of the mobile stations, an occurrence (Ik) of the second location information for locating the mobile station Mk is obtained from the second location estimator, wherein one of: (i) the second location estimator does not include an activation of the occurrence of the first location technique by the first location estimator, and (ii) the occurrence Ik is not substantially affected by any result of the occurrence of the first location technique by the first location estimator.
16. The method of any one of the Claims 1 through 13, wherein the first location estimator does not include an activation of the occurrence of the second location technique activated by the second location estimator, and the second location estimator does not include an activation of the occurrence of the first location technique activated by the first location estimator.
17. The method of any one of the Claims 1 through 16, wherein there is a predetermined site on a communications network wherein said predetermined site performs a step of receiving both said instances of the first location information and said occurrences of the second location information.
18. The method of any one of the Claims 1 through 13, wherein there is a predetermined site on a communications network for performing a step of determining at least one of:
(i) a different location estimate of the mobile station M1 wherein the different location estimate is dependent upon at least one instance of the instances of the first location information for M1, and (ii) identifying one or more geographical structures related to determining a location estimate of the mobile station M1.
19. The method of any one of the Claims 1 through 13, wherein there is a predetermined site on a communications network for performing a step of transmitting the corresponding resulting location estimates for the each of the mobile stations M1 and M2 to their respective destinations on the communications network.
20. The method of any of Claims 9, 14, 17, 18 or 19, wherein the communications network includes a portion of the Internet.
21. The method of any one of the Claims 1 through 13, wherein at least one of the instances of the first location information includes one or more of:
(a) a value indicative of a likelihood of the M1 being at the location estimate represented by the at least one instance of the first location information;
(b) an identifier for identifying M1;
(c) a representation of a likely point location of M1;
(d) a representation of a geographical area containing M1;
(e) an identification of one or more cells of a geographical partition, wherein the cells include a location estimate of M1; and (f) a timestamp indicative of when the wireless signal transmissions were received at the communication stations from M1.
22. The method of any one of the Claims 1 through 13, wherein at least some of said communication stations are provided with base stations of a commercial mobile radio service provider (CMRS), wherein each of said base stations support two way voice communication with the mobile stations.
23. The method of Claim 22, wherein at least some of said communication stations operatively use wireless transceivers at said base stations, wherein said transceivers support the two way voice communication with the mobile stations.
24. The method of Claim 8, wherein said step of determining the corresponding resulting location estimate for the mobile station Mo includes a step of resolving location ambiguities between location estimates of the mobile station Mo provided by the at least one instance of the first location information, and the at least one occurrence of the second location information.
25. The method of Claim 24, wherein said step of resolving includes determining for one of said location estimates for the mobile station Mo, one or more of:
(a) a corresponding likelihood value that said mobile station Mo is within the one location estimate;
(b) a condition related to a corresponding velocity or change of velocity of the mobile station Mo coinciding with the one location estimate;
(c) a condition related to a corresponding terrain of the one location estimate; and (d) a consistency of the one location estimate with a previous location estimate of the mobile station Mo.
26. The method of Claim 24, wherein said step of resolving includes detecting a clustering of at least some of said location estimates for Mo for determining a most likely location of the mobile station Mo.
27. The method of Claim 1, further including a step of determining said corresponding resulting location estimate for M1 by performing a step of adjusting a confidence value for at least one of the first location indications for the instances of the first location information, wherein values for at least some of the following factors are used in adjusting the confidence value: (a) how closely the at least one first location indication matches a predetermined route, (b) how likely an estimated velocity of the mobile station M1 is for a geographical area having the at least one first location indication; (c) how closely the at least one first location indication corresponds to a different estimate for locating the mobile station M1 and (d) how closely the at least one first location indication corresponds to an extrapolated location estimate of the mobile station M1.
28. The method of any one of the Claims 1 through 13, wherein at least one of the instances of the first location information is for the first mobile station M1 and at least one of the occurrences of the second location related information is for the first mobile station M1;
further including determining a preference of the at least one of the instance of the first location information for the first mobile station M1 over the at least one of the occurrence of the second location related information for the first mobile station M1, wherein the corresponding resulting location estimate for M, is dependent upon said preference.
29. The method of Claim 1, wherein the first mobile station M~ is included in a transport, the transport including a third location technique for locating another of the mobile stations.
30. The method of any one of the Claims 1 through 29, wherein said step of receiving includes obtaining at least one of the requests from the Internet.
31. The method of any one of the Claim 1 through 13, wherein said step of receiving includes a request for locating one of the mobile stations for one or more of:
(1) locating a terrestrial vehicle;
(2)locating an emergency caller;
(3)routing a terrestrial vehicle; and (4)locating a child;
(5)locating livestock;
(6)tracking a terrestrial vehicle; and (7)locating a parolee.
32. The method of any one of the Claims 1 through 31, further including a step of determining the corresponding resulting location estimate for the first mobile station M1 by snapping an intermediate location estimate for the first mobile station M1, to a vehicle route near the intermediate location estimate.
33. The method of any one of the Claims 1 through 32, further including a step of determining the corresponding resulting location estimate for the mobile station M1 using one or more of the following:
(a) a value indicative of a consistency between: (i) a collection of wireless signal measurements obtained from wireless communications with M1, and (ii) other collections of wireless signal measurements from a set of one or more of the mobile stations, wherein each collection C of the said other collections has associated therewith a data representation of a geographical location of one of the set of mobile stations substantially from where C was obtained; and (b) a value indicative of a signal strength and signal time delay measurement for wireless signal communications between one of the communication stations and the mobile station M1.
34. A method as claimed in any one of the Claims 1 through 33, wherein the occurrence of the second location technique is an instance of the location technique of T2.
35. A method as claimed in Claim 34, wherein location performance of the second location estimator is improved by data indicative of a range of the mobile station M2 from one of the communication stations, wherein the communication station is stationary.
36. A method as claimed in any one of the Claims 34 or 35, wherein the first and second mobile stations M1 and M2 are the same, and the occurrence of the first location technique is an instance of the location technique of T3.
37. The method as claimed in any one of the Claims 34 or 35, wherein the first and second mobile stations M1 and M2 are the same, and the occurrence of the first location technique is an instance of the location technique of T1.
38. The method as claimed in any one of the Claims 34 or 35, wherein a preference is given to at least one of: (i) one of the instances of the first location information, and (ii) one of the occurrences of the second location information;
wherein the preference is used in determining the corresponding resulting location estimate for M1 as being more dependent upon the least one of: (i) one of the instances of the first location information, and (ii) one of the occurrences of the second location information for which the preference is given.
39. The method as claimed in any one of the Claims 1 through 13, further including the steps of:
transmitting through a communications network, an encoding of said first location estimator from a source site to a site from which the instances of the first location information are obtained.
40. The method as claimed in any one of the Claims 1 through 39, further including a step of determining whether to modify one of: one of the instances of the said first location information, and a corresponding first confidence data, according to at least one of:
(a) an estimated velocity of one of the first mobile station M1;
(b) an estimated acceleration of the first mobile station M1;
(c) a predicted subsequent location of one of the first mobile station M1;
(d) an expected wireless signal characteristic of an area containing the first mobile station M1; and (e) an expected vehicle route of the first mobile station M1.
41. The method as claimed in Claim 40, wherein said step of determining whether to modify includes activating one of an expert system, a fuzzy rule inferencing system and a blackboard daemon.
42. The method as claimed in any one of the Claim 1 through 13, further including a step of determining, for the first mobile station M1, at least one of: a speed of the first mobile station M1, a direction of the first mobile station M1, a change in speed of the first mobile station M1, and a change in direction of the first mobile station M1.
43. The method of any one of the Claims 1 through 42, wherein each of the steps of first and second contacting includes providing to each of the first and second location estimators activation information for activating the first and second location estimators, wherein the activation information is provided to the first and second location estimators via a common data distribution component, wherein said component distributes mobile station location related data to each of the first and second location estimators according to a content of said data expected by the location estimator.
44. The method of any one of the Claims 1 through 43, further including a step of obtaining, for each of a plurality geographical locations, (a1 ) and (a2) following: (a1) a representation of the geographical location, and (a2) for the geographical location, corresponding multipath information indicative of multipath signals previously transmitted between some one of the mobile stations and the communication stations, when the some mobile station transmitted from approximately the geographical location.
45. The method of any one of the Claims 1 through 44, wherein for locating the mobile station M1 wherein M1 is land borne, said location technique T1 includes steps of determining both the distance between the communication station CS and the mobile station M1 and the wireless signal angle of arrival.
46. The method of any one of the Claims 1 through 45, wherein the wireless signal angle of arrival is obtained from the wireless signal measurements obtained from communication stations having an antenna with variable angular reception range.
47. The method of any one of the Claims 1 through 46, wherein the first and second mobile stations M1 and M2 are the same mobile station, but the instances of the first location information are for a first geographic location of the same mobile station, and the occurrences of the second location information are for a second geographic location of the same mobile station, wherein the first and second geographic locations are substantially different.
48. The method of any one of the Claims 1 through 47, wherein at least one of the location techniques T3 and T4 includes a learning technique, wherein said learning technique uses a learned association for associating (a) and (b) following:
(a) information obtained from at least one of signal strength and signal time delay measurements of wireless signals communicated between the mobile station M and the communication stations, and (b) data identifying a likely geographical range for a location for the mobile station M1 wherein said association is learned by a training process using a plurality of data pairs, each said data pair including: first information identifying a known location of some one of the mobile stations, and second information from wireless signal measurements communicated between said some one mobile station and one or more of the communication stations when said some one mobile station is at the known location.
49. The method of any one of the Claims 1 through 48, further including a step of determining one of the corresponding resulting location estimate for at least one of M1 and M2 by using one or more coverage area analysis techniques, wherein each of the coverage area techniques is supplied with input data obtained from wireless signal measurements communicated between: (1) the at least one of M1 and M2, and (2) one or more of the plurality of the communication stations, wherein each said coverage area analysis technique determines for the at least one of M1 and M2, an area containing the at least one of M1 and M2 using at least one of the areas of (i) and (ii) following:
(i) for each communication station CS of one or more of said communication stations that wirelessly detect the at least one of M1 and M2, a corresponding area wherein the communication station CS
is likely to be able to detect the at least one of M1 and M2, and (ii) for each communication station CS of one or more of said communication stations that is wirelessly detected by the at least one of M1 and M2, a corresponding area wherein the communication station CS is likely to be detected by the at least one of M1 and M2.
50. A mobile station location system, comprising:
a predetermined network interface for receiving a plurality of requests from one or more communications networks, wherein each request is for locating, at one or more locations, a corresponding mobile station of a plurality of mobile stations;
wherein the predetermined network interface resides at a site that is independent of movements of the corresponding mobile stations for the requests;
a location estimator determiner for determining which of one or more of a plurality of location estimators, including a first location estimator, and a second location estimator to contact in response to each of said requests, wherein each of said plurality of location estimators outputs location information for at least some of the mobile stations;
wherein for each mobile station M of the plurality of mobile stations for which one of the requests is received, when one or more of said location estimators are supplied with corresponding input data obtained from measurements of wireless signals transmitted between the mobile station M, and at least one of (i) and (ii) following:

(i) at least one of a plurality of terrestrial communication stations operatively configured for at least one of: wirelessly detecting the mobile station M, and wirelessly being detected by said mobile station M, and (ii) one or more non-terrestrial wireless signal transmitting stations which are above and not supported on the Earth's surface, then said one or more location estimators output a corresponding location estimate of a geographical location of the mobile station M;
wherein when the first location estimator outputs a first location information of a location of the mobile station M, said first location information is dependent upon a result from a first component included in one of the component categories (C1) through (C3) below, and wherein when the second location estimator outputs a second location information of a location of the mobile station M, said second location information is dependent upon a result from a second component included in a different one of the component categories (C1) through (C3) from the category of the first component;
wherein the component categories are (C1) through (C3) following:
(C1) a category of locus computing components for estimating mobile station locations, wherein each said locus computing component utilizes measurements (MSMTS) of wireless signals between the mobile station being located and at least one of the terrestrial communication stations CS for determining a locus of mobile station locations from which a mobile station estimate is estimated, wherein said estimate is determined at a site that is not co-located with the mobile station being located, and wherein said measurements MSMTS are a function of at least one of (a), (b) and (c) following:

(a) a signal time delay of wireless signals between the mobile station being located and the at least one communication station CS;
(b) a signal strength of wireless signals between the mobile station being located and the at least one communication station CS;
(c) a direction of arrival of wireless signals indicating an angular orientation about the at least one communication station CS of a direction of wireless transmissions to CS from the mobile station being located;
(C2) a category of signal processing components for estimating mobile station locations, wherein each of the signal processing components determines a mobile station location using timing data obtained from wireless signals, the wireless signals received at a mobile station being located and from one or more of the non-terrestrial transmitting stations, wherein each said signal processing component determines a location of the mobile station being located by determining location related information indicative of one or more offsets of the mobile station being located from the one or more non-terrestrial transmitting stations;
(C3) a category of wireless signal processing components for estimating mobile station locations by performing a step of accessing a collection of associations, wherein for each location L a of a plurality of geographical locations, there is an association of the collection that associates (a1) and (a2) following:

(a1) a representation of the geographical location L a, and (a2) for the geographical location L a, corresponding information indicative of one or more characteristics of wireless signal transmissions transmitted between one of the mobile stations (M a) and at least one of the communication stations, wherein the transmissions are from approximately the geographical location L a, the mobile station M a being different from M; and wherein at least one of the following steps are performed: a step of comparing (b1) and (b2) following, and, a step of determining a similarity in a pattern of wireless signal characteristics between (b1) and (b2) following:
(b1) one or more wireless signal characteristics determined from wireless signals communicated between the mobile station being located and the communication stations, and (b2) the information of (a2) for one or more of the associations;
a predetermined estimator interface for receiving (c) and (d) following:
(c) from the first location estimator, one or more instances of the first location information, each instance including one of (c1) and (c2) following: (c1) a first indication of a location for a first instance (M1) being one of the mobile stations that can be identified as an instance of the mobile station M, and (c2) a second indication of a location for a second instance (M2) being one of the mobile stations that also can be identified as an instance of the mobile station M; and (d) from the second location estimator, one or more occurrences of the second location related information, each occurrence including one of (d1) and (d2) following: (d1) a third indication of a location for the first instance M1, and (d2) a fourth indication of a location for the second instance M2;
an output gateway for transmitting a corresponding resulting location estimate of each of the instances M1 and M2 to a corresponding destination, wherein the corresponding resulting location estimate for the instance M1 is obtained using one or more of the first and third indications, and the corresponding resulting location estimate for the instance M2 is obtained using one or more of the second and fourth indications, and wherein the corresponding destination for the instance M1 corresponds to one of the requests for locating the instance M1, and the corresponding destination for the instance M2 corresponds to one of the requests for locating the instance M2.
51. The location system of Claim 50, wherein the one or more communications networks includes one or more of a telephone network and the Internet, and said output gateway also transmits said resulting location estimate via one or more of a public switched telephone network and the Internet.

206(a)
52. The location system of Claim 51, wherein said location estimator determiner contacts at least one of said first and second location estimators via a transmission on one of a telephone network and the Internet.
53. The location system of Claim 51, wherein said location estimator determiner provides data that is used to route an activation request to said first location estimator.
54. The location system of Claim 53, wherein said activation request is transmitted on the Internet.
55. The location system of Claim 54, wherein said first location information is received by said network interface from an Internet transmission.
56. The location system of Claim 50 or 51, wherein said location estimator determiner provides said corresponding input data to said first location estimator.
57. The location system of Claim 50 or 51 or 56, wherein at least one of the instances of said first location information, and at least one of the occurrences of said second location information are for different locations of the mobile station M1.
58. The location system of Claim 50 or 51, further including a location determiner for determining a likely location estimate for the mobile station M1when said location determiner receives both: one of the instances of said first location information for M1, and one of the occurrences of said second location information for M1, said location determiner including at least one of:

(i) a selector for identifying at least one preferred location estimate from one of: the one instance of said first location information, and the one occurrence of said second location information, said likely location estimate being at least as dependent on said preferred location estimate as any other location estimate from said one instance and said one occurrence; and (ii) a combiner for combining location estimates of M1 obtained from the one instance of aid first location information, and the one occurrence of said second location information for obtaining said likely location estimate.
59. The location system of Claim 58, wherein said location determiner includes at least one ambiguity resolver for resolving an ambiguity between a first location estimate obtained using the one instance, and a second location estimate obtained using the one occurrence.
60. The location system of Claim 59, wherein said ambiguity resolver includes a component for providing:
(a) a different location estimate obtained using said first location estimate wherein said different location estimate more closely conforms with an actual location of M1, and (b) a value indicative of a likelihood of the mobile station M1 being at said different location estimate.
61. The location system of Claim 50 or 51, further including a location enhancing component for enhancing an accuracy of a first location estimate obtained from at least one instance of said first location information, wherein said location enhancing component determines an accuracy enhancing offset of said first location estimate using previous location estimates obtained from said first location estimator.
62. The location system of Claim 50, wherein said first component of said first location estimator is from said category C1, and said first component determines said locus of locations using a measurement indicative of a difference in signal time delays between the mobile station M1 and each of two the communication stations.
63. The location system of Claim 62, wherein said first location estimator performs a trilateration computation for determining the location of M1.
64. The location system of Claim 50, wherein said first location estimator and said second location estimator are not co-located.
65. The location system of any of the Claims 50 through 64, wherein said first location estimator and said second location estimator are both contacted for activation in response to one of the requests for locating the mobile station M1.
66. The location system of any of the Claims 50 through 65, wherein said location estimator determiner provides information for activation of a third location estimator in response to at least one of the requests, wherein the third location estimator provides a different location estimate of M1 from each location estimate included as part of whichever of the first and third indications that are available for determining a location of the mobile station M1.
67. The location system of Claim 50, wherein said location estimator determiner includes a selector for selecting which of the location estimators to contact for locating the M1, wherein said selector performs the selection according to an indication as to an availability of wireless signal measurements used by at least one of the location estimators.
68. The location system of Claim 50 or 51, wherein one or more of said first and second location estimators transmits a mobile station location estimate to an Internet site that operatively utilizes said output gateway.
69. The location system of Claim 50 or 51, wherein for at least one instance of said first location estimator wherein said first component is from said category C1, said first component determines said locus of locations using an angle of arrival of wireless signals indicative of an angular orientation about the at least one communication station CS of a direction of wireless transmissions to CS from M1.
70. The location system of Claim 50, wherein at least one of said non-terrestrial transmitting stations is a satellite.
71. The location system of Claim 50, wherein the first location estimator uses a component from the category (C3) for determining at least instance of the first location information for locating M1.
72. The location system of Claim 71, wherein the component from the category (C3) used by the first location estimator uses wireless signals received, from a plurality of non-terrestrial transmitting stations, by the mobile station M1, wherein a location estimate determined by the component is dependent upon a corresponding spatial range between: (i) M1, and (ii) each of at least two of the non-terrestrial transmitting stations, wherein each of the spatial ranges is determined using a corresponding transmission time for a wireless signal transmitted by a corresponding one of the at least two non-terrestrial transmitting stations and received by the mobile station M1.
73. The location system of Claim 71, wherein the component from the category (C3) used by the first location estimator is improved by data indicative of a range of the mobile station M1 from one of the communication stations, wherein the communication station is stationary.
74. The location system of Claim 50, wherein said predetermined estimator interface provides the instances of the first location information, and the occurrences of the second location information to a collection of one or more predetermined computational components, wherein each of the predetermined computational components performs a mobile station location related computation for determining the corresponding resulting location estimate for each of the mobile stations M1 and M2.
75. The location system of Claim 50, wherein at least one of the instances of the first location information is for the first instance of M1 and at least one of the occurrences of the second location related information is for the first instance of M1;
wherein the at least one instance of the first location information and the at least one occurrence of the second location related information are one of: (i) for substantially a same location, and (ii) substantially different locations.
76. The location system of Claim 50, wherein at least one of the instances of the first location information is for the first instance of M1 and at least one of the occurrences of the second location related information is for the first instance of M1;
further including a component for determining a preference of the at least one of the instance of the first location information for the first instance of M1 over the at least one of the occurrence of the second location related information for the first instance of M1, wherein the resulting location estimate corresponding to M1 is dependent upon said preference.
77. The location system of Claim 76, wherein said at least one of the instance of the first location information for the first instance of M, is given preference over the at least one of the occurrence of the second location related information for the first instance of M1, and said at least one of the instance of the first location information for the first instance of M1 and the first component is included in the component category (C2).
78. The location system of any of the Claims 50 through 77, wherein the resulting location estimate corresponding to M1 has a timestamp associated therewith, wherein the timestamp is indicative of when the resulting location estimate corresponding to M1 is applicable as a location of M1.
79. The location system of any of the Claims 50 through 78, wherein the resulting location estimate corresponding to M1 is obtained as a function of a position of a known geographical feature that is identified as sufficiently close to a location of M1.
80. The location system of any of the Claims 50 through 79, wherein when at least one of the instances of the first location information is for the first instance of M1 and at least one of the occurrences of the second location related information is for the first instance of M1, each of the at least one of the instance of the first location information for the first instance of M1, and the at least one of the occurrence of the second location related information for the first instance of M1 is substantially unaffected by the other.
81. The location system of any of the Claims 50 through 80, wherein each of the mobile stations M1 and M2 are in two way wireless communication with a commercial mobile radio service provider via at least one base station of the commercial mobile radio service provider.
CA002265875A 1996-09-09 1997-09-08 Location of a mobile station Expired - Lifetime CA2265875C (en)

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US2585596P 1996-09-09 1996-09-09
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US4482197P 1997-04-25 1997-04-25
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US5659097P 1997-08-20 1997-08-20
US60/056,590 1997-08-20
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