Calibration of a device location measurement system that utilizes wireless signal strengths
First Claim
1. A device location calibration system comprising:
- at least three transmitter components that each broadcast a signal;
a receiver component that measures and records a signal characteristic of each of the signals, the signal characteristics are associated with a spatial location that the signal characteristics were measured at, the signal characteristics include at least one of a signal strength, a phase, an autocorrelation, or a spectrum; and
a regression component that generates a regression function, the regression function maps a plurality of signal vectors to a plurality of estimated spatial locations, each signal vector includes at least one measurement of the signal characteristic, wherein the regression function comprises an isotropic Gaussian kernel function that employs the vectors.
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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.
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Citations
20 Claims
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1. A device location calibration system comprising:
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at least three transmitter components that each broadcast a signal; a receiver component that measures and records a signal characteristic of each of the signals, the signal characteristics are associated with a spatial location that the signal characteristics were measured at, the signal characteristics include at least one of a signal strength, a phase, an autocorrelation, or a spectrum; and a regression component that generates a regression function, the regression function maps a plurality of signal vectors to a plurality of estimated spatial locations, each signal vector includes at least one measurement of the signal characteristic, wherein the regression function comprises an isotropic Gaussian kernel function that employs the vectors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method to calibrate a device location system comprising:
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receiving at least three signals; measuring and recording signal characteristics of the signals, the signal characteristics are associated with a spatial location that the signal characteristics were measured at, the signal characteristics include at least one of a signal strength, a phase, an autocorrelation, or a spectrum; and generating a regression function, the regression function maps a plurality of signal vectors to a plurality of estimated spatial locations, each signal vector includes at least one measurement of the signal characteristics, wherein the regression function comprises an isotropic Gaussian kernel function that employs the vectors. - View Dependent Claims (13, 14, 15, 16, 17)
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18. A device location calibration system comprising:
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means for measuring and recording at least one signal associated with a spatial location that signal characteristics were measured at, the signal characteristics include at least one of a signal strength, a phase, an autocorrelation, or a spectrum; and means for generating a regression function, the regression function maps a plurality of signal vectors to a plurality of estimated spatial locations, each signal vector includes at least one measurement of the signal characteristic, wherein the regression function comprises an isotropic Gaussian kernel function that employs the vectors. - View Dependent Claims (19, 20)
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Specification