An indoor multi-source fusion positioning approach based on PDR/MM/WiFi

Abstract

The use of smartphones for indoor positioning has become increasingly popular in recent years. The cumulative error is an unavoidable problem for pedestrian dead reckoning (PDR). Mismatching is the main problem for fingerprint matching. Therefore, how to integrate multiple sensors to reduce the position error and improve the robustness of the positioning system is a subject worthy of further research. For fingerprint matching, an enhanced dynamic time warping (EDTW) is proposed to improve the accuracy of magnetic field matching. For PDR/magnetic matching (MM)/WiFi, a multi-source fusion positioning approach based on a robust extended Kalman filter (REKF) that introduces the estimation of the innovation sequence covariance is studied. The PDR-based error model is taken as the state transition equation and the difference between PDR and MM and the difference between PDR and WiFi as the observation equation. The experimental results show that the multi-source fusion positioning approach not only reduces the position error but also improves the robustness.