Pedestrian dead reckoning for MARG navigation using a smartphone

Abstract

Abstract The demand for navigating pedestrian by using a hand-held mobile device increased remarkably over the past few years, especially in GPS-denied scenario. We propose a new pedestrian dead reckoning (PDR)-based navigation algorithm by using magnetic, angular rate, and gravity (MARG) sensors which are equipped in existing commercial smartphone. Our proposed navigation algorithm consists of step detection, stride length estimation, and heading estimation. To eliminate the gauge step errors of the random bouncing motions, we designed a reliable algorithm for step detection. We developed a BP neural network-based stride length estimation algorithm to apply to different users. In response to the challenge of magnetic disturbance, a quaternion-based extended Kalman filter (EKF) is introduced to determine the user's heading direction for each step. The performance of our proposed pedestrian navigation algorithm is verified by using a smartphone in providing accurate, reliable, and continuous location tracking services.