Abstract

Aiming at the problem of low speed and positioning fluctuations of indoor WiFi fingerprints. Firstly, we use the method of Gauss fitting and averaging to acquire the average value of the received signal. Secondly, we use a distance to be similarity measure to define a threshold to classify the fingerprint database. Finally, By improving the K nearest neighbor algorithm and on the basis of classification, Implement fast matching of K nearest neighbor. The experimental results show that the time efficiency of the classified location system has been greatly improved, with an average decrease of 62.8%; In the positioning accuracy, WiFi fingerprint positioning of the average error from 4.17 m down to 2.12 m.

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