WiFi Signal Fingerprint Localization Based on KNN and Particle Filter Fusion Algorithm

Abstract

WiFi signal fingerprint indoor location usually uses KNN algorithm, but the single algorithm is prone to accidental error, and the location accuracy of the algorithm is very difficult to meet the indoor positioning needs. Location tracking, a position estimation by the position fingerprint method, the optimal weighted value according to the Kalman filter algorithm, but in front of the non Gauss noise filtering algorithm, Kalman appeared to be inadequate, Monte Carlo particle filtering algorithm to predict particle state is applicable to all nonlinear Gauss noise. In this paper, we tried to match the location fingerprint information by KNN algorithm, and then predicted the particle state and weight by particle filter algorithm to optimize the location results, so as to improve the positioning accuracy of WiFi signal fingerprint location method. The experimental simulation results showed that the positioning accuracy of WiFi fingerprint is obviously improved by selecting the appropriate number of particles through the two algorithms.