University of Chinese Academy of Sciences, Beijing100094, China.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2019 Sep 13;19(18):3962. doi: 10.3390/s19183962.
The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system's initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments.
零速更新(ZUPT)辅助扩展卡尔曼滤波器(EKF)常用于传统基于惯性导航系统(INS)的足部行人航位推算(PDR)系统中,它可以有效抑制惯性导航系统的行人导航误差增长。然而,在实际测试中,系统仍然经常会出现漂移,这通常是由两个原因造成的:动态行人步态中静止阶段的检测失败和ZUPT 方法中难以观测的航向漂移。在本文中,首先,为了提高初始航向对准精度,提出了一种基于几何方法的新方法来校准 PDR 系统的初始绝对航向。通过使用校准线而不是仅使用起点的航向,该方法可以更准确地校准 PDR 系统的初始航向。其次,针对动态行人步态中静止阶段检测失败的问题,提出了一种新的静止阶段检测方法,该方法基于脚部运动周期性,而不是传统方法中的阈值比较原理。在实验中,我们发现零速状态点总是出现在每个步态周期中静止检测器的最小值附近。通过在每个步态周期中取最小值作为零速状态点,可以有效地减少零速间隔的检测失败。最后,为了减少随时间行走过程中的航向漂移,基于范围约束原理提出了一种新的运动约束方法。在行人行走过程中,当前时刻的脚部位置估计与上一个静止期之间的距离应在最大步长范围内。一旦距离大于最大步长,约束方法将用于将当前估计的脚部位置约束到相对于上一个静止脚部位置的最大步长的球体范围内。最后,通过实验验证了所有提出方法的有效性。