Source: http://www.google.nl/patents/US20040176108
Timestamp: 2017-12-13 01:15:49
Document Index: 133101273

Matched Legal Cases: ['art 2006', 'art 2015', 'art 2016', 'art 2013', 'art 2009', 'art 2010', 'art 2006', 'art 2013']

Patent US20040176108 - Location applications for wireless networks - Google Patenten
A method for locating terminals (T) in a wireless network (RN) comprising several base stations. A probabilistic model (PM) of the wireless network indicates a probability distribution (413 1-413 n) for signal values of several base stations at several locations in the wireless network. Each terminal...http://www.google.nl/patents/US20040176108?utm_source=gb-gplus-sharePatent US20040176108 - Location applications for wireless networks
Publicatienummer US20040176108 A1
Aanvraagnummer US 10/365,621
Publicatiedatum 9 sept 2004
Aanvraagdatum 13 feb 2003
Prioriteitsdatum 13 feb 2003
Ook gepubliceerd als US7149531, WO2004073343A1
Publicatienummer 10365621, 365621, US 2004/0176108 A1, US 2004/176108 A1, US 20040176108 A1, US 20040176108A1, US 2004176108 A1, US 2004176108A1, US-A1-20040176108, US-A1-2004176108, US2004/0176108A1, US2004/176108A1, US20040176108 A1, US20040176108A1, US2004176108 A1, US2004176108A1
Uitvinders Pauli Misikangas
Oorspronkelijke patenteigenaar Pauli Misikangas
Patentcitaties (9), Verwijzingen naar dit patent (84), Classificaties (6), Juridische gebeurtenissen (9)
US 20040176108 A1
A method for locating terminals (T) in a wireless network (RN) comprising several base stations. A probabilistic model (PM) of the wireless network indicates a probability distribution (413 1-413 n) for signal values of several base stations at several locations in the wireless network. Each terminal makes a set of observations (423) of signal values of a subset of the several base stations. The terminal's location (425) is estimated based on the probabilistic model (PM) and the set of observations. The terminal recurrently measures at least one additional item (424), which is not used for locating the terminal. Each information tuple (42) indicates the additional items (424) and the time (422) and the location (425) at which the additional items were measured. The information tuples (42) are used to determine the additional item (424) as a function (45) of time and location in the wireless network.
1. A method for locating several terminals in a wireless network comprising several base stations;
using the stored information tuples to determine the at least one additional item as a function of time and location in the wireless network.
5. A method according to claim 1, wherein the at least one additional item comprises throughput in the radio network.
6. A method according to claim 4, wherein the at least one additional item is measured in response to detecting a simultaneous need and opportunity to measure the at least one additional item.
7. A method according to claim 5, wherein the step of measuring throughput comprises, in the following order:
the terminal continuing making the set of observations;
inversely weighting the throughput measurement by an estimated change of the terminal's location between the discontinuing and continuing steps.
8. A method according to claim 1, wherein the at least one additional item comprises identifiers of base stations.
9. A method according to claim 8, further comprising using the identifiers of base stations and the estimate of the terminal's location to detect an illegitimate base station.
10. A method according to claim 1, wherein the step of maintaining the probabilistic model of the wireless network comprises physically measuring signal values of several base stations at several locations in the wireless network.
11. A method according to claim 1, wherein the step of maintaining the probabilistic model of the wireless network comprises calculating signal values of several base stations at several locations based on a propagation model of the wireless network.
storing information tuples, wherein each information tuple indicates the at least one additional item and the time and the estimate of the terminal's location at which the at least one additional item was measured; and for
14. An arrangement according to claim 13, wherein the control means comprises a controller for coordinating measurements of several terminals.
15. An arrangement according to claim 14, wherein the at least one additional item comprises throughput in the radio network.
16. An arrangement according to claim 15, wherein the controller is operable to order a terminal to perform the following acts:
17. An arrangement according to claim 14, wherein the at least one additional item comprises identifiers of base stations.
18. An arrangement according to claim 13, wherein the arrangement further comprises multiple device models that compensate for the differences between different terminals' observations of the at least one signal quality value; and
19. A computer program product embodying computer instructions, wherein executing the computer instructions in a computer arrangement functionally connected to a wireless network serving terminals causes the computer arrangement, the wireless network and the terminals to perform the steps of claim 1.
[0003]FIG. 1 schematically illustrates an example of such a positioning technique. A terminal T communicates via base stations BS via a radio interface RI. In this example, the communication is assumed to be radio communication. The terminal T observes signal values at the radio interface RI. Observations O are applied to a probabilistic model PM that models the terminal's wireless communication environment and produces a location estimate LE. As used herein, a terminal is a device whose location is to be determined. The terminal communicates via signals in a wireless network, and signal values in the wireless network are used for determining the terminal's location. For example, the terminal may be a data processing device communicating in a wireless local-area network (WLAN). The data processing device may be a general-purpose laptop or palmtop computer or a communication device, or it may be a dedicated test or measurement apparatus such as a hospital instrument connected to the WLAN. A location, as used herein, is a coordinate set comprising one to three coordinates. In some special cases, such as tunnels, a single coordinate may be sufficient but in most cases the location is expressed by a coordinate pair (x, y or angle/radius).
Another aspect of the invention is an arrangement for carrying out this method. The arrangement comprises means for measuring at least one additional item at the terminal's location, wherein the at least one additional item is not comprised in said set of observations. The measuring means depend on the nature of the additional item(s) being measured. If the additional item is the signal strength of base stations that are not being used to locate the terminal, or network throughput, no additional hardware is required, and the measuring means may comprise only software routines. On the other hand, if the additional item is an environmental variable, additional hardware, such as sensors and analogue-to-digital converters, is usually required. But measuring an environmental variable per se poses no great difficulties to a person with ordinary skill in the art, and a detailed description may be omitted.
A preferred embodiment of the invention relates to visualizing the measurements in the wireless networks. The visualizing comprises weighting the measurements made at different times with a time-dependent weight function. From the set of prior observations Hj k at sample point sk, a probability distribution (such as a histogram or the like) of variable vj ε VSS for a given point in time t0 can be calculated such that more recent observations have stronger weight than older ones. In order to do so, we define a weight function f(t, t0) that returns the weight of observations made at time t when the moment of interest (such as the present) is time t0. By way of example, such a weight function fcan be of the form f(t, t0)=α51 t0-t|/β, wherein the parameters 0<α<1 and β>0 determine the rate at which the weight of an observation decreases with time. Thus the final weight of observation oj ki in the distribution is wki·f(tki, t0).
A further preferred embodiment comprises calculating a weighted average age (average distance from the present time t0) of the measurements at each sample point sk ε S as: t 0 - ( ∑ i  t ki · w ki · f  ( t ki , t 0 ) / ∑ i  w ki · f  ( t ki , t 0 ) )
[0042]FIG. 1 schematically illustrates a positioning technique;
[0043]FIG. 2 shows a location estimation module LEM for estimating a terminal's location based on signal values at the radio interface RI;
[0044]FIG. 3 illustrates an arrangement that comprises a common controller for controlling measurements; and
[0045]FIG. 4 illustrates various information flows in a preferred embodiment of the invention.
[0046]FIG. 2 is a block diagram of an exemplary location estimation module LEM for estimating a terminal's location based on signal values at a radio interface RI. FIG. 2 shows a compact location estimation module LEM, but more distributed embodiments are equally possible. An essential feature of the location estimation module is a probabilistic model PM of the terminal's wireless network, the probabilistic model being able to predict the terminal's location given a plurality of observations from the radio interface. In this example, the probabilistic model PM is built and maintained by a model construction module MCM. The model construction module MCM builds and maintains the probabilistic model on the basis of calibration data CD or propagation data PD in the form of one or more propagation models, or any combination thereof. Calibration data CD is the result of physically measuring signal values at known locations (or determining the coordinates of those locations if not known by other means). Optionally, the calibration data records may also comprise the time at which the observation was made, in case the signal parameters vary with time. Instead of the calibration data CD, or in addition to them, one or more propagation models PD can be used to model the radio interface RI. The propagation models can be constructed by techniques that are analogous to ray-tracing techniques for visual simulation. The locations at which calibration measurements are collected are called calibration points. The calibration data CD comprises data records each of which comprises the location of the calibration point in question and the set of signal parameters measured at that calibration point. The location can be expressed in any absolute or relative coordinate system. In special cases, such as trains, highways, tunnels, waterways or the like, a single coordinate may be sufficient, but normally two or three co-ordinates will be used.
[0049]FIG. 3 illustrates an arrangement that comprises a common controller for controlling measurements. Such a common controller is needed for network throughput measurements, for example, because the actions of several terminals must be monitored and/or coordinated. Network throughput is one example of the one or more additional items measured in step 75 of FIG. 7. In the embodiment shown in FIG. 8, the radio network RN comprises a controller CTRL and the radio network RN is connected via a router R to a data network DN, such as the Internet.
[0054]FIG. 4 illustrates various information flows in a preferred embodiment of the invention. The probabilistic PM model contains several sample points. Reference numeral 41 generally denotes a representative record in the data section of the probabilistic model PM. Reference numeral 411 denotes a sample point identifier, which is only needed for internal data processing operations. Reference numeral 412 denotes the coordinates (in some two or three-directional coordinate system) of the geographical location corresponding to the sample point. The z coordinate may be an actual third-dimension coordinate, or it may be interpreted as discrete floor or level information. Reference numeral 413 1, through 413 n denote the signal value distributions of various base stations BS1 through BSn, along with the associated history and weights, as described earlier. An optional field 414 indicates the time of the last update of the sample point in question. For example, the contents of the time field 414 may reflect a probability-weighted average of time stamps 422 of the terminals' observations.
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Classificatie in de VS 455/456.5, 455/423
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