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首页> 外文期刊>Nature reviews Cancer >Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor
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Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

机译：基于低成本脚踏MEMS-IMU /超声波传感器的行人导航系统性能提升

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摘要

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.
机译：基于惯性导航系统的行人导航系统（PNS）扩展卡尔曼滤波器零速度更新（INS-EKF-ZUPT或IEZ）广泛用于复杂环境，而由于其自主和连续性的特征，没有外部基础设施。然而，IEZ遭受了由过程噪声统计信息的动态变化引起的性能劣化和标题估计误差。本研究的主要目的是基于低成本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稳健性。所提出的解决方案具有两个主要组成部分：（1）利用模糊推理系统（FIS）在解决创新和理论上的统计样本协方差与模糊的统计样本协方差之间的不匹配后，为扩展卡尔曼滤波器（EKF）产生自适应因素，以及模糊提出了基于MEMS-IMU /超声波传感器的自适应EKF（FAEKF）。因此，自适应因子被应用于校正准确反映先前状态估计的过程噪声协方差。 （2）开发了一种直动标题更新（SMHU）算法以检测是否发生直接步道并在超声波传感器检测到墙壁的脚和反射点之间的距离，修改标题的错误。实验结果表明，与使用高端MEMS-IMU的其他作品相比，水平定位误差小于不同环境中的总行驶距离（TTD）的2％，这是与其他作品相同的定位误差顺序。得出结论是，在准确性和稳健性方面，该方法可以为PNS实现高性能。

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