Probabilistic model for a positioning technique

• US 7,196,662 B2
• Filed: 11/30/2004
• Issued: 03/27/2007
• Est. Priority Date: 05/31/2002
• Status: Active Grant

• Current Assignee: AiRISTA Flow, Inc. (AiRISTA, LLC), AiRISTA International Oy (AiRISTA, LLC)
• Original Assignee: Ekahau Oy
• Inventors: Misikangas, Pauli, Myllymaki, Petri
• Primary Examiner(s): Tarcza; Thomas H.
• Assistant Examiner(s): MULL, FRED H
• Application Number:
  US10/999,140
• Publication Number:
  US 20050128139A1
• Time in Patent Office: 847 Days
• Field of Search: 342/451, 4554561-457
• US Class Current: 342/451
• CPC Class Codes: G01S 3/06 Means for increasing effective directivity, e.g. by combining signals having differently oriented directivity characteristics or by sharpening the envelope waveform of the signal derived from a rotating or oscillating beam antenna comparing amplitude of signals having differently oriented directivity characteristics to determine direction G01S3/16, G01S3/28
• CPC Class Codes: G01S 5/02528 Simulating radio frequency fingerprints
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A model construction module (MCM) for constructing a probabilistic model (PM) of a wireless environment (RN) in which a target device (T) communicates using signals that have a measurable signal value (x), such as signal strength. The model construction module forms several submodels (611–631) of the wireless environment (RN). Each submodel indicates a probability distribution (F₁–F₃) for signal values at one or more locations (Q₁–Q_Y) in the wireless environment. The module combines the submodels to a probabilistic model (PM) of the wireless environment (RN), such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations includes combining the inverse cumulative distribution functions of the submodels or existing locations.

Claims

1. A method for estimating a target device'"'"'s location, wherein the target device is operable to move in a wireless environment and to communicate with the wireless environment using signals each of which has at least one measurable signal value, the method comprising:

forming a plurality of submodels of the wireless environment, each submodel indicating a probability distribution for signal values at one or more locations in the wireless environment;

combining the submodels to provide a probabilistic model of the wireless environment, the probabilistic model indicating a probability distribution for signal values at several locations in the wireless environment;

making a set of observations of signal values in the wireless environment at the target device'"'"'s location; and

estimating the target device'"'"'s location based on the probabilistic model and the set of observations,

wherein the combining of the submodels comprises;

for each submodel, forming a cumulative distribution function and weighting the cumulative distribution function with a relative weight;

forming a combination of the weighted cumulative distribution functions; and

forming the probability distribution for the probabilistic model based on the combination of the weighted cumulative distribution functions.

2. A method according to claim 1, wherein the plurality of submodels comprises a plurality of calibration submodels such that each calibration submodel relates to exactly one calibrated location.

3. A method according to claim 2, further comprising:

inserting into the probabilistic model sample, points for which calibration measurements are not available, by interpolating or extrapolating based on two or more calibrated locations; and

assigning a relative weight to each calibrated location based on the inserted sample point'"'"'s distance from the calibrated location such that the relative weight and distance are in inverse relationship to each other.

4. A method according to claim 1, wherein the plurality of submodels comprises at least one propagation model.

5. A method according to claim 1, wherein the location-estimating is performed in the target device.

6. A method according to claim 1, wherein the location-estimating is performed in a fixed equipment to which the target device reports the sequence of observations via a radio network.

7. A method according to claim 1, wherein the at least one measurable signal value comprises signal strength.

8. A method according to claim 1, wherein the at least one measurable signal value comprises bit error rate or ratio.