In this paper, we propose a novel method for pedestrian dead reckoning (PDR) using microelectromechanical system magnetic, angular rate, and gravity sensors, which includes a double-step unscented Kalman filter (DUKF) and hidden Markov model (HMM)-based zero-velocity-update (ZVU) algorithm. The DUKF divides the measurement updates of the gravity vector and the magnetic field vector into two steps in order to avoid the unwanted correction for the Euler angle error. The HMM-based ZVU algorithm is developed to recognize the ZVU efficiently. Thus, the proposed PDR method can reduce the position drift caused by the heading error and fault zero-velocity measurement. Experimental results demonstrate that the proposed method achieves better yaw estimate, as well as zero-velocity measurement, and obtains more accurate dead-reckoning position than other methods in the literature.

中文翻译：

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使用 MEMS 传感器进行行人航位推算的双步无迹卡尔曼滤波器和基于 HMM 的零速度更新

在本文中，我们提出了一种使用微机电系统磁、角速率和重力传感器进行行人航位推算 (PDR) 的新方法，其中包括双步无迹卡尔曼滤波器 (DUKF) 和基于隐马尔可夫模型 (HMM) 的零-速度更新（ZVU）算法。DUKF 将重力矢量和磁场矢量的测量更新分为两个步骤，以避免对欧拉角误差进行不必要的校正。开发了基于 HMM 的 ZVU 算法以有效识别 ZVU。因此，所提出的 PDR 方法可以减少由航向误差和故障零速测量引起的位置漂移。实验结果表明，所提出的方法实现了更好的偏航估计，以及零速度测量，