Pedestrian navigation system is an important application of inertial sensor technology because it does not depend on any external facilities. However, the positioning accuracy is limited due to the cumulative error of the inertial sensor. Zero Velocity Update (ZUPT) is an effective method to compensate the cumulative error. To avoid the jitter and random noise defects of single inertial sensor, a zero-position-difference ZUPT combined with foot and shank kinematics information is proposed in this work. Firstly, the zero position difference constraint matrix with ankle joint as the connection point is established through analyzing the local motion characteristics of foot and shank. In order to get accurate attitude and position by fusing the measurement information of foot and shank, an improved extended Kalman particle filter (EKPF) based on the zero position difference is proposed. Moreover, according to the gait characteristics of foot and shank, a weight assignment strategy is designed to detect the zero velocity intervals of walking. The feasibility and effectiveness of the algorithm are verified by experiments. The results show that the error of the improved zero velocity detector is about 22% less than that of the foot-mounted zero velocity detector. The improved EKPF method reduces the positioning error by more than 35% and 45% compared with the traditional EKPF method based on shank and foot, respectively.

中文翻译：

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脚柄式行人惯性导航系统的零位差 ZUPT 方法


行人导航系统不依赖于任何外部设施，是惯性传感器技术的重要应用。然而，由于惯性传感器的累积误差，定位精度受到限制。零速度更新（ZUPT）是补偿累积误差的有效方法。为了避免单个惯性传感器的抖动和随机噪声缺陷，本文提出了一种结合足部和小腿运动学信息的零位置差 ZUPT。首先，通过分析足部和小腿的局部运动特性，建立以踝关节为连接点的零位差约束矩阵。为了融合足部和小腿的测量信息得到准确的姿态和位置，提出了一种基于零位置差的改进扩展卡尔曼粒子滤波器（EKPF）。此外，根据足部和小腿的步态特征，设计了权重分配策略来检测步行的零速度区间。通过实验验证了该算法的可行性和有效性。结果表明，改进后的零速探测器的误差比脚装式零速探测器的误差减少了约22%。改进的EKPF方法与传统的基于小腿和足部的EKPF方法相比，分别降低了35%和45%以上的定位误差。