The indoor localization method based on the integration of RSSI and inertial sensor

Abstract

The research of localization has become a more and more important topic with the popularity of ubiquitous mobile computing. In indoor environment, since the global positioning system (GPS) is disabled, many miniaturized wireless and sensing technologies have shown giant potential in positioning applications such as Inertial Navigation. In this context, this paper present a methodology to locate and track pedestrians accurately in indoor scenarios, the proposed method employs the extended Kalman filter (EKF) to integration Received Signal Strength Indication (RSSI) measurements with the Inertial Navigation technology. Aiming at the cumulative errors existed in Pedestrian Dead Reckoning (PDR) algorithm, this method uses RSSI information as measurement vector of EKF to correct the cumulative errors. Experimental results show that the proposed fusion method can present more reliable positioning estimations.