In this paper, we propose a high accuracy indoor positioning method that uses residual magnetism in addition to Pedestrian Dead Reckoning (PDR) and WiFi-based localization methods. Our proposed method needs WiFi and magnetic field fingerprints, which are created by measuring in advance the WiFi radio waves and the magnetic field in the target map. The fingerprints are represented by a Gaussian Mixture Models (GMMs) to reduce the amount of computation.
Our proposed method estimates positions by comparing the pedestrian sensor and fingerprint values by particle filters. We evaluated this method in real environments and confirmed that it provides accurate indoor positioning with a mean error less than 8 m and more accurate position detection than existing techniques.