WLAN indoor positioning method based on gradient boosting and particle filtering

Abstract

Indoor positioning technology has shown its great application prospects in smart cities. The main purpose of this paper is to study a low-cost, low-error indoor positioning method that can get a accurate indoor position when communicating with wireless local area networks (WLAN). The paper optimises the traditional WLAN indoor positioning method based on location fingerprint database, and algorithms about in indoor signal simulation, similarity matching of vector and continuously-positioning are tested in work of this paper, and a WLAN indoor positioning method based on gradient boosting and particle filtering is proposed. The paper finally shows the indoor positioning result with an average error of 1.7 metres. These research results verify the feasibility of WLAN indoor positioning and show that the positioning accuracy will be improved with the further optimisation of the positioning method. The potential application values of WLAN technology make it more convenient for internet of things (IoT) technology.