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Abstract：
The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.
摘要：
基于惯性导航系统-扩展卡尔曼滤波-零速度更新（INS-EKF-ZUPT或IEZ）的行人导航系统（PNS）由于其自主性和连续性的特点，被广泛应用于没有外部基础设施的复杂环境中。IEZ,然而,由于过程噪声统计和航向估计误差的动态变化而导致性能下降。本研究的主要目标是基于低成本的脚踏微机电系统惯性测量单元（MEMS-IMU）和超声波传感器的集成，有效地提高行人定位的准确性和鲁棒性。所提出的解决方案具有两个主要组成部分：（1）在解决了创新的统计样本协方差与理论协方差之间的不匹配之后，利用模糊推理系统（FIS）为扩展卡尔曼滤波器（EKF）生成自适应因子，提出了基于MEMS-IMU/超声传感器的模糊自适应EKF(FAEKF)行人传感器。因此，自适应因子用于校正准确反映先前状态估计的过程噪声协方差。（2）开发了一种直线运动航向更新（SMHU）算法，以检测是否发生直线行走，并在超声波传感器检测到脚与墙壁反射点之间的距离时修正航向误差。实验结果表明，在不同环境下，水平定位误差小于总行进距离(TTD)的2%，与使用高端MEMS-IMU的其他作品相比，这是相同的定位误差顺序。结论是，该方法可以在准确性和鲁棒性方面实现PNS的高性能。
