WO2013171679A1

WO2013171679A1 - Handheld-device-based indoor localization system and method

Info

Publication number: WO2013171679A1
Application number: PCT/IB2013/053954
Authority: WO
Inventors: Fabian HÖFLINGER; Johannes WENDEBERG; Leonhard Reindl; Christian SCHINDELHAUER
Original assignee: Albert-Ludwigs-Universität Freiburg
Priority date: 2012-05-15
Filing date: 2013-05-15
Publication date: 2013-11-21
Also published as: EP2850451A1

Abstract

The invention relates to the field of indoor localization systems. In particular, the invention relates to indoor localization systems using handheld-devices, which use sound impulses emitted from said handheld devices. According to the invention, the system for localizing a mobile handheld device (1) comprises at least one mobile handheld device (1) capable of creating sound signals (2) comprising a unique identifier (5) to be emitted from a speaker of said mobile handheld device (1), a multitude of stationary receiver devices (3A, 3B, 3C) capable of detecting said sound signals (2) and generating time stamps (4) consisting of the time points of said sound signals (2), and communicating with each other and with a server (6), said server (6) being capable of calculating the position of the mobile handheld device (1), and of wirelessly transmitting a position information (7) signal of said position to the mobile handheld device (1). The invention further discloses a method for localizing a mobile handheld device (1) relatively to known positions of a network of at least three stationary receivers (3A, 3B, 3C), and method for the automatic calibration of a system as disclosed.

Description

Handheld-device-based indoor localization system and method

Introduction

The invention relates to the field of indoor localization systems. In particular, the invention relates to indoor localization systems using handheld-devices, which use sound impulses emitted from said handheld devices.

State of the art

For localization of people in indoor areas the satellite- supported localization systems (e.g. GPS) cannot be used due to a missing line-of-sight to the GPS satellites. Thus, alternative technologies are required.

Special electromagnetic wave systems use ultra-wideband (UWB) radio to localize objects or people with high accuracy in indoor areas. Such systems are sold e.g. by Ubisense solutions (see www.ubisense.net) . For these systems, the user of the mobile device needs additional hardware, which is often impractical in real world scenarios, and also costly.

A number of existing systems use smart phones for localization by using the Received Signal Strength Indicator (RSSI) method, see e.g. P. Bahl and V. Padmanabhan, "Radar: an in-building rf-based user location and tracking system," in INFOCOM 2000, Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Proceedings IEEE, vol. 2, 2000, pp. 775-784. Using these method people can be navigated with low accuracy (1.5 m - 3 m) . However, these systems are susceptible to errors in dynamic environments, i.e. environments with moving obstacles. For example, the RSSI value is depending on the distance and on the environment. The RSSI value is distorted by objects in the direct path and in the vicinity and by environmental influences like air humidity. Another method is to use the ToF (Time of Flight) or RTT (Round Trip Time) measurement to measure the distances from immobile, so called anchor nodes to a mobile device. However, there are several intrinsic uncertainty factors of the measurement methods which lead to the inaccuracy regarding distance measurement. Especially by using these measurement methods for COTS (commercial off-the-shelf) smart phones, there exists a variable latency, resulting from a continuously changing misalignment or offset between the timestamps of the command from the transmitted signal and the transmitted signal from the loudspeaker or microphone. A comparable problem results from the synchronization between the smart phones and the anchor nodes (infrastructure) . These delays can easily add up to several milliseconds, which imply a ranging localization error of several centimeters . Documents US 2012/0214515 Al and US 2013/0083631 disclose systems using sound signals, received by the anchor nodes, which are received from the mobile devices acting as emitters. However, this method suffers from certain disadvantages:

The microphone of COTS smart phones can only detect relatively low frequencies (i.e. frequencies in the audible range) due the limitation of its built in microphone (made for normal speaking which uses the band between 80 Hz and 12 kHz) . Outside this frequency range the microphone has low sensitivity to receive sound from larger distances. Additionally there exists a maximum sampling rate of the analog to digital converter of COTS smart phones. The corresponding sampling frequency needs to be greater than twice of the maximum signal frequency. As a result, the sound emitted by the handheld device lies in the audible range, detectable by the user. Furthermore, this frequency band is crowded with natural sounds, making it more difficult to distinguish the localization signal from noise. Due to permanently receiving the sound signals by the mobile device, increased power consumption is necessary for signal identification and calculation.

Another disadvantage is the resulting effort necessary to prepare a system before its actual use. In known systems, for the calibration of the system the positions of the so called "anchor nodes" (receivers) usually must be known, i.e. measured, which is usually achieved by a standard multilateration algorithm. Therefore, the installation effort for the provider of such a system is significant.

Object of the invention

The object of the invention is to provide a system for indoor- localization which avoids the disadvantages known from the art.

In particular, the system shall provide high accuracy and robustness when navigating people for example between exhibition stands or to products in supermarkets.

The cost for the user of the mobile device should be minimal, and no additional hardware shall be necessary for him.

The additional power consumption of the mobile device shall be reduced.

Since the number of anchor nodes depends on the size of the building, and in large buildings, more than 50 anchor nodes might be necessary, the installation effort for the provider shall be reduced to a minimum.

Description

The system provided by the present invention is used for localizing a mobile handheld device, i.e. a device which can be carried around by a subsequently called "user". The system comprises at least one mobile handheld device which is capable of creating "sound signals", i.e. signals in a frequency band of 10 to 50 kHz, to be emitted from a loudspeaker of said mobile handheld device. In particular, the frequency range is not limited to sounds audible by humans. The sound signals are subsequently also called "chirps".

The sound signals comprise a unique identifier which is contained (encoded) within the sound signal in the form of a sequence of so called "symbols", i.e. a representation of a number of bits. The unique identifier, or short "ID", serves for the purpose of distinguishing the mobile device from others which might be part of the system at the same time.

The system further comprises a multitude of stationary receiver devices which are capable of detecting said sound signals and generating time stamps consisting of the time points of said sound signals. In other words, the stationary receivers can detect the presence of a specific handheld device and determine the exact time when its sound signal reaches the receiver. The receivers are capable of communicating with each other, preferably by a wireless, e.g. GSM/UMTS/LTE/WiFi, or wired, e.g. Ethernet/USB/Firewire/Homeplug connection .

Furthermore, the receivers are capable of communicating with a server. This server can be a stand-alone device, or it can be functional part of one or more of the receivers. It can also be part of a cloud service or the like, i.e. a pure software architecture .

According to the invention, the server is capable of calculating the position of the mobile handheld device, and of wirelessly transmitting a position information signal of said calculated position to the mobile handheld device. The receivers and the server are capable of synchronizing their clocks. The server may provide a "master time" to which the receivers adjust themselves.

Due to the fact that the system uses stationary receivers, the microphones of these receivers can be designed specifically for the task at hand, i.e. sensitivity for inaudible frequencies. At the same time, the microphones of the handheld devices need not to fulfill any specific requirements; in fact, the mobile devices need no microphones at all. Another advantage of having the receivers stationary lies in the fact that for stationary receivers, the problem of ambient noises resulting from mechanical contact between the user and the device can not occur. In contrast, for mobile receivers, especially when the device is worn inside a pocket, ambient noises can become significant. As a result, the present invention improves the efficiency and robustness of a localization system.

The localization system can be used everywhere where people or mobile objects such as AGVs (automated guided vehicles) need to be navigated towards a target. The proposed invention introduces a novel method for localization of handheld devices in indoor areas .

Envisaged applications are for example finding gates in airports or in supermarkets to find specific products or in museums and fairs to find places. Furthermore, it can be used for measuring the dimensions of objects and rooms in buildings with the help of e.g. smart phones.

According to a particularly preferred embodiment, the frequency of the sound signal is above the audible range of a human and preferably ranges between 18 kHz and 21 kHz or 23 kHz. As a result, humans, being are the main addressees of the invention, will not be disturbed by the sound signals produced by the system. Since this frequency band is much less crowded with natural sounds than audible frequencies, it is much less difficult to distinguish the localization signal from noise.

Particularly preferred, the mobile handheld device is a smart phone, i.e. a so called "COTD" (common off-the shelve) device. Typically, such a device has a microphone limited to the frequency range audible, and also typically, producible by humans .

As experiments have surprisingly shown, speakers of such smart phones are capable of producing sounds in the range between 18 and 21 kHz, or even 23 kHz. At the same time, it was found out that the microphones of these devices could not detect such sounds. As a result, mobile handheld devices which are smart phones are not usable as receivers for inaudible sound signals, but they serve sufficiently as emitters for such signals. This fact makes it possible to transmit short sound pulses from customary handheld devices to receivers without disturbing humans .

As will be described in detail later, the position of the handheld device is received from a server. An "app" of the handheld device optionally visualizes these positions in context of the environment, with a map and surrounding items, on the screen of the handheld device.

Preferably, every registered handheld device in a room is assigned a unique ID by the infrastructure (server unit) . Alternatively, the ID can e.g. be the unique IMEI number of a mobile phone, or a MAC address of network hardware present in the mobile device. These IDs are "statically" and need only be propagated into the system by the mobile device.

According to preferred embodiment, the mobile handheld device is capable of displaying its position information visually, acoustically and/or tactically to the user. As a result, the user gets information back from the server, so that not only the server calculates the position of the handheld device, but the user of this device can make personal use of this information.

A scenario where it might be sufficient to keep the position information available at the server site for third party use is in security systems. In emergency situations, rescue teams need to exactly know how many and where people are located inside a building with e.g. restricted visibility.

However, it is also preferred that the position can be displayed together with optional additional information, as will be described later on, to the user on the handheld device.

It is clear that the transfer of this information preferably takes place via wireless communication (see above) . It is also clear that the handheld device as well as the server must comprise according hardware necessary for said wireless communication. In case that the server is physically integrated with one or more receivers, they can share their communication hardware .

Preferably, a system as described above has a the mobile handheld device which comprises a storage medium with a software, wherein this software is capable of controlling the speaker of the mobile handheld device in order to produce the aforementioned sound signals, and wherein the mobile device is further capable of equipping said signals with the aforementioned unique identifier, and optionally of receiving the position information of the mobile handheld device.

In other words, the system comprises a storage medium (RAM, ROM, memory card or the like) which contains a software that controls hardware parts of the handheld device which is in particular a smart phone, so that no additional hardware is necessary on the end user side, because all necessary functions are provided by the software and the usual smart phone hardware (loudspeaker, display) . The invention is also directed towards a software for a mobile handheld device as defined above, wherein the software is capable of performing the tasks as defined in the foregoing paragraph. The user has to install the software ("app") on his handheld device, which preferably is a smart phone. The interface for the user is as simple as starting the app, which connects to a server and receives an ID using his usual internet connection. The mobile handheld device is assigned specific parameters (unique ID) , such that several devices can be distinguished by the appearance of their sound signals ("chirps") .

The invention also discloses a method for localizing a mobile handheld device relatively to known positions of a network of at least three stationary receivers. It comprises the following steps : 1. Emitting sound signals comprising a unique identifier by the handheld device.

2. Detecting these sound signals (2) by the receivers.

3. Providing time stamps consisting of the measured exact time of the received sound signal by the receivers. 4. Communicating this time stamps together with the unique identifier to a server unit.

5. Calculating the position of the mobile handheld device by the server unit.

In fact, the accuracy of the localization system relies on precise synchronization between the receivers. The connected receiver clients negotiate one master receiver which acts as a time reference. Then, the other clients adjust their clocks to the master. The calculation is done in an adaption of the Network Time Protocol algorithm. Both, the time offset and the timer drift are considered. With a IEEE 802.11 b/g Wi-Fi connection, a synchronization precision of better than 0.1 ms can be achieved.

In other words, the handheld device (s) transmit the sound signals to the receivers. The chirp signals are received by sound receiver devices which detect the specific sounds of each mobile handheld device which preferably is a smart phone. At the receiver, a cross-correlation between the received signal and each symbol is performed. Highly measured peaks indicate the time of arrival. According to a preferred embodiment, the fixed (stationary) receivers are connected in a wired or wireless network (e.g. Wi- Fi network) , such that they can easily synchronize their clocks and exchange the time differences of arrived signals of the received chirps. The receiver (s) generate global timestamps for the received sound signal.

Subsequently, the receiver transmits the received timestamps and the ID via network to an server unit. This server can be a separate computer, or the receivers can act as server, alternatively. Preferably, the server unit calculates the position of the handheld device with a "Time Difference of Arrival" (TDoA) algorithm. By knowing the propagation speed of sound and the arrival times at the receivers, the position of the handheld device can be calculated. In case that also the mobile device is synchronized with the receivers, other algorithms such as time-of-flight can be used as well .

According to a preferred embodiment, the TDoA position estimation is enhanced through analysis of the incoming sound signal strength. This means, that the accuracy of the localization can be improved. To minimize wrong measured distances produced from multipath propagations, a plausibility test can be carried out, using e.g. a probabilistic filter algorithm.

According to a preferred embodiment, the unique identifier is provided by the server unit during an initial sign-in step of the mobile handheld device. This sign-in step can e.g. occur when the user enters the building in which the localization shall take place. Preferably, the unique identifier is transmitted wirelessly (WLAN, GSM, NFC, ...) , but it can also be typed in manually or scanned in using barcode or the like, displayed on paper or on a screen connected to the server.

According to another embodiment, the position to the mobile handheld device is transmitted subsequently to its calculation from the server unit to the mobile handheld device. This is done by providing this position with the according unique identifier e.g. in a computer network accessible by mobile handheld device which then selects the according position matching its unique identifier. In short, the server unit transmits the position information of the handheld device, preferably together with a map of the room, to the handheld device. Regarding advantages of this embodiment, reference is made to the explanations above.

According to another embodiment, a local map, text, graphics, and/or audible information regarding the environment of the mobile handheld device are being transmitted to the handheld device from the server unit. This information can e.g. stem from dedicated services such as advertisers for local shops or the like, or it can stem from search machines which contain information about the surrounding of certain positions. In order to correlate the position of the smart phone and the available database information, GPS coordinates can be calculated by the server and used as reference for the current position which additional information is looked for. According to another embodiment, the mobile device emits only short impulses (e.g. 10 ms of the sound signal, followed by longer breaks (e.g. 300 ms) , thus reducing its power consumption while transmitting. In other words, the pulse duty factor can range between 1:1 and 1:1000, and preferably between 1:20 and 1:50.

The generation of sound signals can preferably be quitted when a certain command is sent from the server, e.g. because the user leaves the "gate" of the system, or it can simply quit when no position data is received for a certain time, indicating that the user is likely to be beyond the covered area of the system, so that the receivers cannot detect the sound signals any more.

In another embodiment, the sound signal comprises encoded digital information, e.g. by way of representing different bits using a rising and falling frequency sweep ("chirp spread spectrum", CSS) .

In particular, the digital information relates to the unique identifier (ID) of the mobile handheld device.

CSS is robust against multipath fading. The signal from the smart phones reaches the receiver with several echoes and reflections from the objects in indoor areas due to multipath propagation.

The reflections reach the receiver in/out of phase which generates a greater or lower amplitude (interferences) . This leads to a poor identification of the time stamps from the line of sight (LOS) signals. By using a chirp spread spectrum, the amplified and attenuated signals are in balance because all energy shares are collected.

The invention also relates to a method for the automatic calibration of a system as defined above, wherein sound signals comprising an unique identifier are provided by a moving mobile handheld device, detected by at least three stationary synchronized receivers which generate time stamps of these signals, and transfer the unique identifier together with the according time stamps to a server unit which calculates the positions of said receivers.

In other words, since the aforementioned method for localizing a mobile handheld device requires known positions of the receivers, a calibration method is presented in order to achieve these positions with no manual interaction for a provider of the system. In particular, the positions must neither be measured nor entered into the system (i.e. the server and/or the receivers) . In fact, a system equipped with the according software can calibrate itself automatically and autonomously.

A Time Difference of Arrival (TDoA) algorithm is used for calculating the position of the receivers by the server unit.

As mentioned before, the invention is capable of using a setting wherein the positions of the transmitter and receivers are unknown. The only source of information is the time when the smart phones' chirps are received. The discrete signals occur at unknown positions and times, but measures are taken that they can be distinguished from each other. The clocks of the receivers are synchronized, so the time differences of arrival (TDoA) of the signals can be computed. The goal is to estimate the relative positions of all receivers.

Preferably, the calculation of the positions of said receivers is achieved by means of a self-calibrating TDoA algorithm using a non-linear optimization approach and a probabilistic state estimation algorithm (particle filter and/or Kalman filter), that solves a non-linear equation system imposed by the received sound signals as defined above.

There are several self-calibrating TDoA algorithms available to calculate the positions of the receivers (anchors, anchor nodes) . Localization without the receiver positions and relying only on TDoA can be solved if assumptions on the signal positions are made, i.e. the signals originate from far away (see e.g. T. Janson, C. Schindelhauer, and J. Wendeberg: „Self-Localization Application for iPhone using only Ambient Sound Signals", Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp 259-268, Nov. 2010). Biswas and Thrun propose a solution which is close to the problem setting (R. Biswas and S. Thrun: "A Passive Approach to Sensor Network Localization", Proceedings of the International Conference on Intelligent Robots and Systems, 2004 (IROS 2004), 2004 IEEE/RSJ, vol. 2, pp 1544-1549, 2004). No assumptions of the signal positions are required and only TDoA information is used to iteratively refine a Bayesian network. However, the correct positions of the receivers cannot be found in every case. Another approach was proposed by Pollefeys and Nister (M. Pollefeys and D. Nister: "Direct computation of sound and microphone locations from time difference-of-arrival data", IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. pp 2445-2448, IEEE, 2008) . The special case of ten or more receivers is solved in a linear approach without initialization and without assumptions on the position.

In the present invention, the so called "Iterative Cone Alignment algorithm" solves iteratively a non-linear optimization problem of time differences of arrival (TDoA) by a physical spring-mass simulation. The success rate of solving the calculation of the receiver positions was increased in comparison to the gradient descent method, using only six received signals and four receivers. Another algorithm that is used in the system is based on probabilistic state estimation of the receiver and the sender, here the handheld device. By using a sequential Monte-Carlo sampling method, also known as particle filter, and/or by using a Kalman filter, the configuration of the receivers is robustly calculated during the calibration phase. The two algorithms constitute a novel development in the field of TDoA localization that allows for creation of a quick-setup system for smart phone localization. By using these self-calibrating TDoA algorithms, in contrast to conventional TDoA localization algorithms, there is no need to measure the positions of the receivers by external means, which is time-consuming and tedious.

In short, preferably, the calculation of the positions of said receivers is achieved by means of an iterative optimization and probabilistic state estimation by the Sequential Monte-Carlo method and by the Kalman Filter.

Finally, the invention is directed towards a method for localizing a mobile handheld device and using a system as defined above, wherein the method comprises an initial automatic calibration step as defined above, followed by the localization steps as defined above. In other words, the method preferable comprises all necessary steps for setting up (calibrating) and then running the system according to the invention.

The presented system and method avoid the drawbacks known from the art. In particular, it provides high accuracy and robustness, and the cost for the user of the mobile device is minimal, since no additional hardware needs to be provided on the handheld device side. The additional power consumption of the mobile device is reduced to a minimum, since all calculation effort is shifted to the server. Also, the installation effort for the provider is reduced to a minimum, since the system is capable of self-calibration.

Description of figures

Figure 1 shows an implementation example of the method. Figure 2 shows a detailed overview of a receiver. Figure 3 shows the measured frequency response of the built-in loudspeakers of different smart phones.

Figure 4 shows a "Spread Spectrum": Sequence of five chirps with varying frequency, consisting of two different symbols "up" and "down".

Figure 5 shows a runtime example of the sequential Monte-Carlo method auto-calibrating.

Figure 6 shows the example of fig. 5 after sufficient long calibration time. In Fig. 1, an implementation example of the method according to the invention is shown. A mobile handheld device 1 such as a smart phone generates specific chirp impulses (sound signals 2), preferably in a frequency range of 18-21 kHz. The signal 2 contains also a unique identifier (ID) 5 of the handheld device 1. The receivers 3A, 3B and/or 3C receive these sound impulses 2 and generate time stamps 4 of the arrival time via synchroniza^¬ tion (network time 9) . These time stamps 4 together with the according ID 5 are communicated to the server 6. The position information 7 of the mobile handheld device 1 is then calculated using an iterative multilateration algorithm by the server 6. Subsequently, position information 7 of the handheld device 1 is transmitted to the handheld device 1 and shown at its display, or acoustically signaled (e.g. using speech).

In Fig. 2 shows a detailed overview of one receiver 3A. The sound impulses 2 containing the ID 5 are received by a microphone 8 with a bandwidth preferably up to 21 kHz or more, at least slightly within in the (for humans) inaudible range. The impulses 2 are then filtered, amplified and digitalized by an ADC (Analog Digital Converter) . A microcontroller ("yC") detects and analyzes the chirp impulses 2 and sends the information of the ID 5 with the synchronization time (time stamp 4) to a server unit (not shown) . The receiver 3A is capable to communicate with other receivers (not shown) for synchronization purposes of network time 9.

In Fig. 3, the measured frequency response of the built-in loudspeakers of different smart phones is depicted. Thus, with commercially available smart phones, it is possible to create sounds up to 21 kHz or higher. As a result, these sounds will not be audible for humans. Thus, COTS smart phones are perfectly suitable for being used as mobile handheld devices for the systems and method provided. In Fig. 4, a "Spread Spectrum" is shown. The exemplary sequence consists of five chirps with varying frequency, consisting of two different symbols "up" and "down". By way of representing different bits using a rising and falling frequency sweep ("chirp spread spectrum", CSS) , the unique identifier 5 can be encoded into the sound signal 2.

In Fig. 5 and Fig. 6, a runtime example of the sequential Monte- Carlo method ("particle filter") is shown while auto-calibrating, therefore localizing, the positions of eleven receivers and of one handheld device (all reference numerals omitted) . While fig. 5 shows the situation after a short time of calibration, fig. 6 shows the situation after a sufficient long time (10 time steps later) . The scatter-plots symbolizing the calculated positions of the receivers converge into rather small spots. The path of the moving mobile handheld device 1 is also shown as continuous line between the surrounding receivers (reference numerals omitted) . Reference numerals

1 mobile handheld device, smart phone

2 sound impulse, chirp

3A, 3B, 3C receiver

4 time stamp

5 ID, unique identifier

6 server, server unit

7 position information

8 microphone

9 network time

Claims

Patent claims

1. A system for localizing a mobile handheld device (1), comprising at least one mobile handheld device (1) capable of creating sound signals (2) comprising a unique identifier (5) to be emitted from a speaker of said mobile handheld device

(1), a multitude of stationary receiver devices (3A, 3B, 3C) capable of detecting said sound signals (2) and generating time stamps (4) consisting of the time points of said sound signals (2), and communicating with each other and with a server (6), said server (6) being capable of calculating the position of the mobile handheld device (1), and of wirelessly transmitting a position information (7) signal of said position to the mobile handheld device (1) .

2. A system according to claim 1, wherein the frequency of the sound signal (2) is above the audible range and preferably between 18 kHz and 21 kHz.

3. A system according to claim 1 or 2, wherein the mobile handheld device (1) is a smart phone.

4. A system according to any of claim 1 to 3, wherein the mobile handheld device (1) is capable of displaying said position information (7) visually and/or acoustically and/or tactically .

5. A system according to any of claims 1 to 4, wherein the mobile handheld device (1) comprises a storage medium with a software, this software capable of controlling the speaker of the mobile handheld device (1) in order to produce said sound signals (2), and capable of equipping said sound signals (2) with said unique identifier (5) , and receiving the position information (7) of the mobile handheld device (1) . 6. A software for a mobile handheld device (1) as defined in claim 1, wherein the software is capable of performing the tasks as defined in claim 5. A method for localizing a mobile handheld device (1) relatively to known positions of a network of at least three stationary receivers (3A, 3B, 3C) , comprising the following steps :

- emitting sound signals (2) comprising a unique identifier (5) by the handheld device (1);

- detecting these sound signals (2) by the receivers (3A, 3B, 3C) ;

- providing time stamps (4) consisting of the measured exact time of the received sound signal (2) by the re^¬ ceivers (3A, 3B, 3C) ;

- communicating these time stamps (4) together with the unique identifier (5) to a server unit (6);

- calculating the position of the mobile handheld device (1) by the server unit (6); wherein the receivers (3A, 3B, 3C) have synchronized clocks available .

A method according to claim 7, wherein a Time Difference of Arrival (TDoA) algorithm is used for calculating the position of the mobile handheld device (1) by the server unit (6) .

A method according to claim 8, wherein the TDoA position estimation is enhanced through analysis of the incoming sound signal strength.

A method according to any of claims 7 to 9, wherein the unique identifier (5) is provided by the server unit (6) during an initial sign-in step of the mobile handheld device (1) .

A method according to any of claims 7 to 10, wherein the position to the mobile handheld device (1) is transmitted subsequently to its calculation from the server unit (6) to the mobile handheld device (1) .

12. A method according to any of claims 7 to 11, wherein the server unit (6) transmits additional information selected from the group consisting of a local map, text, graphics, and audible information regarding the environment of the mobile handheld device (1) .

13. A method according to any of claims 7 to 12, wherein the mobile device (1) emits short impulses of the sound signal (2) followed by longer breaks.

14. A method according to any of claims 7 to 13, wherein the sound signal (2) comprises encoded digital information.

15. Method for the automatic calibration of a system as defined in claim 1, wherein sound signals (2) comprising an unique identifier (5) are provided by a moving mobile handheld device (1), detected by at least three stationary synchro- nized receivers (3A, 3B, 3C) which generate time stamps (4) of these signals (2), and transfer the unique identifier (5) together with the according time stamps (4) to a server unit (6) which calculates the positions of said receivers (3A, 3B, 3C) using a TDoA algorithm. 16. Method for the automatic calibration according to claim 15, wherein the calculation of the positions of said receivers (3A, 3B, 3C) is achieved by means of a self-calibrating TDoA algorithm using a non-linear optimization approach and a probabilistic state estimation algorithm, that solves a non- linear equation system imposed by the received sound signals (2) as defied in claim 18.

17. A method for localizing a mobile handheld device (1) as defined in claim 1 using a system as defined in claim 1, characterized in that an initial automatic calibration step as defined in claim 15 or 16 is followed by the localization steps as defined in any of claims 7 to 14.