Implementation of hybrid indoor positioning system based on Wi-Fi and PDR in smartphone

Abstract

This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking.