AN INTEGRATED INDOOR POSITIONING ALGORITHM FOR SMARTPHONE USING PEDESTRIAN DEAD RECKONING WITH MAGNETIC FINGERPRINT AIDED

Abstract. Indoor positioning has gained increasing attention in previous decades. There are many wireless communication technologies replacing Global Navigation Satellite System (GNSS) because of the sheltered GNSS signal in indoor environment. Most of them need to set up external transceiver with high spatial density to get user's position, such as Wi-Fi, Ultra-Wideband (UWB), Bluetooth and so on. In order to reach high positioning accuracy, the cost of external transceiver becomes higher.

This research focuses on low-cost pedestrian dead reckoning (PDR) without additional external equipment. Moreover, a magnetic fingerprint-based positioning is adopted to provide redundant observations of position and heading, using Extended Kalman Filter (EKF) to update the estimation. The proposed method reduces cumulative errors of PDR, achieving an improved algorithm. Since the geomagnetic field exists over the Earth, this technology doesn’t use external equipment either. The integration of PDR and magnetic fingerprint-based positioning, which only uses built-in sensors of a smartphone, should be a low-cost and wide coverage scheme.