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