An Adaptive Location Estimator Based on Kalman Filtering for Dynamic Indoor Environments

Abstract

This paper presents algorithms for calibrating and tracking the location of a mobile terminal based on radio propagation modeling (RPM) and Kalman filtering for indoor wireless local area networks (WLANs). In this Kalman filter-based (KF-based) tracking algorithm, the observed location information is extracted from the empirical and RPM positioning methods. Not only can the proposed RPM algorithm calibrate the change of different environmental conditions in a real dynamic environment but also the KF-based tracking algorithm can reduce the location error with smaller sampling time and vanquish the phenomenon of the aliasing in the signal space. Our experimental results show that more than 90 percent of the estimated locations have error distances less than 2.3 meters.