Robust Biomechanical Model-Based 3-D Indoor Localization and Tracking Method Using UWB and IMU

This paper proposes a robust sensor fusion algorithm to accurately track the spatial location and motion of a human under various dynamic activities, such as walking, running, and jumping. The position accuracy of the indoor wireless positioning systems frequently suffers from non-line-of-sight and multipath effects, resulting in heavy-tailed outliers and signal outages. We address this problem by integrating the estimates from an ultra-wideband (UWB) system and inertial measurement units, but also taking advantage of the estimated velocity and height obtained from an aiding lower body biomechanical model. The proposed method is a cascaded Kalman filter-based algorithm where the orientation filter is cascaded with the robust position/velocity filter. The outliers are detected for individual measurements using the normalized innovation squared, where the measurement noise covariance is softly scaled to reduce its weight. The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The proposed algorithm was validated against an optical motion tracking system for both slow (walking) and dynamic (running and jumping) activities performed in laboratory experiments. The results show that the proposed algorithm can maintain high accuracy for tracking the location of a subject in the presence of the outliers and UWB signal outages with a combined 3-D positioning error of less than 13 cm.

本文提出了一种鲁棒的传感器融合算法，以在诸如步行、跑步和跳跃等各种动态活动下准确跟踪人体的空间位置和运动。室内无线定位系统的定位精度经常受到非视距和多径效应的影响，导致出现重尾异常值和信号中断。我们通过整合超宽带（UWB）系统和惯性测量单元的估计值，并利用从辅助下身生物力学模型获得的估计速度和高度来解决这个问题。所提出的方法是一种基于级联卡尔曼滤波器的算法，其中方向滤波器与鲁棒的位置/速度滤波器级联。使用归一化新息平方对单个测量值进行异常值检测，其中测量噪声协方差被软性缩放以降低其权重。通过劳赫 - 通 - 斯特里贝尔平滑器进一步提高定位精度。所提出的算法在实验室实验中针对慢速（步行）和动态（跑步和跳跃）活动通过光学运动跟踪系统进行了验证。结果表明，所提出的算法在存在异常值和UWB信号中断的情况下能够保持高精度跟踪对象的位置，综合三维定位误差小于13厘米。