Calibration of a device location measurement system that utilizes wireless signal strengths

• US 7,233,286 B2
• Filed: 01/30/2006
• Issued: 06/19/2007
• Est. Priority Date: 04/25/2003
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Platt, John C., Krumm, John C.
• Primary Examiner(s): Issing; Gregory C.
• Application Number:
  US11/342,515
• Publication Number:
  US 20060119516A1
• Time in Patent Office: 505 Days
• Field of Search: 342/451
• US Class Current: 342/451
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01S 5/02528 Simulating radio frequency fingerprints
• CPC Class Codes: H04W 16/20 for indoor coverage or short range network deployment
• CPC Class Codes: H04W 16/225 for indoor or short range network
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management
• CPC Class Codes: H04W 88/18 Service support devices; Network management devices

Abstract

An architecture for minimizing calibration effort in an IEEE 802.11 device location measurement system. The calibration technique is based upon a regression function that produces adequately accurate location information as a function of signal strength regardless of gaps in the calibration data or minimally available data. The algorithm takes a set of signal strengths from known room locations in a building and generates a function giving (x,y) as a function of signal strength, which function may then be used for the estimation of new locations. Radial basis functions, which are simple to express and compute, are used for regression. The fact that the algorithm maps signal strength to continuous location makes it possible to skip rooms during calibration, yet still evaluate the location in those rooms.

Claims

1. A computer implemented method of calibrating a system used to determine new location(s) depending on observed signal properties, comprising the following computer executable acts:

measuring signal phases, autocorrelations and spectrums related to identified locations; and

generating a regression function based at least in part upon the signal phases, autocorrelations and spectrums, the function estimating new location(s) as a function of observed signal phases, autocorrelations and spectrums, wherein the regression function is an isotropic Gaussian kernel function, and wherein a vector comprising multiple vectors of signal phases, autocorrelations and spectrums appended together, is input to the isotropic Gaussian kernel function.

2. The method of claim 1, the vectors are computed by clustering a plurality of vectors of measured signal phases, autocorrelations and spectrums.

3. The method of claim 1, the regression function further comprises additional parameters that are computed from a centroid of the identified locations.

4. The method of claim 1, the signal phases, autocorrelations and spectrums are of wireless radio signals transmitted from wireless transceivers.

5. A computer implemented method of calibrating a wireless device location measurement system, comprising the following computer executable acts:

accessing at least one wireless transmitting device;

logging signal phases, autocorrelations and spectrums data for each of wireless transmitting device; and

generating a regression function based upon the signal phases, autocorrelations and spectrums data, the regression function utilized to estimate new location(s), wherein the regression function is an isotropic Gaussian kernel function, and wherein a vector comprising multiple vectors of signal phases, autocorrelations and spectrums appended together, is input to the isotropic Gaussian kernel function.

6. The method of claim 5, the signal phases, autocorrelations and spectrums data logged from a plurality of different locations within range of the transmitting device.

7. A location system that uses observed signal properties to determine new location(s), comprising a computer processor for executing the following software components, the system is recorded on a computer-readable medium and capable of execution by a computer, comprising:

a measuring component for measuring signal phases, autocorrelations and spectrums related to identified locations; and

a regression component for generating a regression function based at least in part upon the signal phases, autocorrelations and spectrums, the function estimating new location(s) based upon observed signal phase, autocorrelation and spectrum data, wherein the regression function is an isotropic Gaussian kernel function, and wherein a vector comprising multiple vectors of signal phase, autocorrelation and spectrum data is appended together, and input to the isotropic Gaussian kernel function.

8. The system of claim 7, the regression function comprising weights, and the regression component comprising a fitting component for fitting the regression function to the measured signal properties by adjusting the weights.

9. The system of claim 8 the fitting component comprising a least-squares fitting component.

10. The system of claim 8, the fitting component comprising a component for computing a kernel matrix.

11. The system of claim 8, the fitting component receives as input an estimate of the location uncertainty associated with each identified location.

12. The system of claim 8, the weights comprising a plurality of weights for computing an x location and a plurality of weights for computing a y location.

13. The system of claim 8, the regression function further comprising additional parameters that are computed from the centroid of the identified locations.

14. The system of claim 7, the signal phases, autocorrelations and spectrums are wireless signals transmitted from wireless transceivers.

15. A computer readable medium having stored thereon the computer executable measuring component and regression component of the system of claim 7.