Fusion positioning algorithm of indoor WiFi and bluetooth based on discrete mathematical model

Abstract

Aiming at the problem of limited beacon coverage and low positioning accuracy when indoor WiFi and Bluetooth are positioned separately, the fusion positioning algorithm of indoor WiFi and Bluetooth based on discrete mathematical model is studied. The position fingerprint method is used to realize WiFi indoor positioning through two stages of “off-line/training” and “on-line positioning”. The intensity of Bluetooth is obtained through Gauss distribution model. The intensity of Bluetooth signal and the relationship between the distance corresponding to Bluetooth and signal intensity are used to obtain the positions of multiple groups of points to Bluetooth nodes. And then the final positioning coordinates of users are obtained by centroid algorithm. The prior location results and distribution are obtained by multi-source prior information fusion between WiFi and Bluetooth. The optimal Bayesian posterior distribution density function is used to estimate the coordinate deviation, which is used to correct the fusion positioning results and obtain the optimal estimation of the coordinates of WiFi and Bluetooth fusion positioning. The experimental results show that the algorithm can locate indoor volunteers with a positioning accuracy of more than 98% and a positioning time of less than 5 s, which has a high positioning performance.