LOCATION DETERMINATION USING GENERALIZED FINGERPRINTING
First Claim
1. A method comprising:
- constructing a training data set and a test data set by using input data comprising location information;
using the training data set and the test data set to generate a first set of performance analytics, the first set of performance analytics generated by executing a first location determination procedure;
using the training data set and the test data set to generate a second set of performance analytics, the second set of performance analytics generated by executing a second location determination procedure; and
determining from at least the first set of performance analytics and the second set of performance analytics that one of the first location determination procedure or the second location determination procedure performs better than the other of the first location determination procedure or the second location determination procedure.
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Accused Products
Abstract
An RF fingerprinting methodology is generalized to include non-RF related factors. For each fingerprinted tile, there is an associated distance function between two fingerprints (the training fingerprint and the test fingerprint) from within that tile which may be a linear or non-linear combination of the deltas between multiple factors of the two fingerprints. The distance function for each tile is derived from a training dataset corresponding to that specific tile, and optimized to minimize the total difference between real distances and predicted distances. Upon receipt of an inference request, a result is derived from a combination of the fingerprints from the training dataset having the least distance per application of the distance function. Likely error for the tile is also determined to ascertain whether to rely on other location methods.
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Citations
20 Claims
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1. A method comprising:
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constructing a training data set and a test data set by using input data comprising location information; using the training data set and the test data set to generate a first set of performance analytics, the first set of performance analytics generated by executing a first location determination procedure; using the training data set and the test data set to generate a second set of performance analytics, the second set of performance analytics generated by executing a second location determination procedure; and determining from at least the first set of performance analytics and the second set of performance analytics that one of the first location determination procedure or the second location determination procedure performs better than the other of the first location determination procedure or the second location determination procedure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system comprising:
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a memory associated with a computing device, said memory storing data comprising one or more location observations and one or more non-RF related factors; and a processor programmed to; execute a dataset constructor to construct a training data set and a test data set using the one or more location observations and the one or more non-RF related factors; execute a plurality of experiments to generate comparative analytics, wherein executing a first experiment of the plurality of experiments comprises; applying a first modeling algorithm and a first location inference algorithm to at least a portion of the test data set; and generating a first set of accuracy analytics from applying the first modeling algorithm and the first location inference algorithm to the at least a portion of the test data set; and further wherein executing a second experiment of the plurality of experiments comprises; applying a second modeling algorithm and a second location inference algorithm to the at least a portion of the test data set; and generating a second set of accuracy analytics from applying the second modeling algorithm and the second location inference algorithm to the at least a portion of the test data set; and execute one or more analytic scripts on each of the first and the second set of accuracy analytics to obtain respective location, tile, and world accuracy analytics. - View Dependent Claims (13)
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14. A method comprising:
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constructing a training data set and a test data set each comprising a plurality of fingerprints; representing a location by a plurality of tiles; associating each tile of the plurality of tiles with a distance function, the distance function based, at least in part, on a delta between two or more factors of a plurality of fingerprints in the training data set; determining a first number of nearest fingerprints in the training data set on the basis of a first distance function for a first tile, wherein the first number of nearest fingerprints is selected from the training data set on the basis of an inference request; determining an error distance curve for the first tile based on the first distance function and at least a portion of the test data set; and when the error distance curve indicates at least a predetermined level of accuracy, creating cache data for the first tile, wherein the cache data provides location information in accordance with a first procedure for location determination. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification