Indoor Localization using PDR with Wi-Fi Weighted Path Loss Algorithm

Abstract

Localization using pedestrian dead reckoning (PDR) and received signal strength indication (RSSI) signals from Wi-Fi enhance the indoor positioning accuracy by reducing the inertial measurement unit (IMU) sensor errors and Wi-Fi RSSI signal fluctuations in an indoor scenario. The indoor position accuracy not only depends on the effective utilization of IMU sensors in PDR systems but also the Wi-Fi RSSI signal acquisition in Wi-Fi positioning systems during non-line of sight conditions. In this paper, we propose a position estimation algorithm for indoor localization using PDR with Wi-Fi weighted path loss (WPL) algorithm. The proposed model follows a PDR system with accelerometer, gyroscope and magnetometer sensors. The PDR system estimates the indoor position results from the complementary actions of accelerometer, gyroscope and magnetometer sensors. The Wi-Fi positioning system uses the WPL algorithm in the proposed model for compensating the PDR system errors and improves the indoor position accuracy. The proposed model also uses a linear kalman filter (LKF) for PDR and Wi-Fi sensor fusion. Experimental results show that the proposed indoor localization approach using PDR with Wi-Fi WPL achieves a high positioning accuracy as compared to the individual positioning systems. The proposed model significantly outperforms individual positioning systems in terms of position accuracy and achieves an improved average localization error of 1.45 m when a user moves in a rectangular motion.