A Wi-Fi based Pedestrian Navigation System using Kalman Filter and Particle Filter

Abstract

This paper presents a Wi-Fi based pedestrian navigation system in indoor environment. The proposed system is constructed by a mobile phone and several Wi-Fi Access Points(AP). User carries the phone on foot while moving in indoor environment at normal speed without any external positioning techniques. The Wi-Fi Access Point are settled in the building, while calculating the distance between transmitter and receiver with the received signal strength (RSS) method. Since the radio wave propagates with the inverse-square law, distance can be calculated based on the relationship between transmitted and received signal strength. The user can be located by Weighted Circular Algorithm. The aim of this work is to acquire accurate results by implementing tow kinds of filters in the positioning framework. Tracking filters are integrated to reduce estimation errors causing by radio signal attenuation and multi-path effect. Regarding to the constant velocity model, Kalman Filter(KF) and Particle Filter(PF) are effective approaches to reduce the estimation errors. Experimental results have successfully validated the effectiveness of proposed positioning algorithms and its associated tracking filters.