A Full-State Robust Extended Kalman Filter for Orientation Tracking During Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units
A Full-State Robust Extended Kalman Filter for Orientation Tracking During Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units
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DOI：
10.1109/tnsre.2021.3093006
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发表时间：
2021-01-01
影响因子：
4.9
通讯作者：
Rouhani, Hossein
中科院分区：
文献类型：
--
作者：
Nazarahari, Milad;Rouhani, Hossein
多年来，在关节角度和行人航位推算系统的长时间测量中，使用磁性和惯性测量单元（MIMU）的准确且鲁棒的定向估计一直是一个挑战，并且限制了MIMU的几个实际应用。因此，本研究旨在开发一种全状态鲁棒扩展卡尔曼滤波器（REKF），用于MIMU的精确和鲁棒的方位跟踪，特别是在长时间动态任务期间。首先，我们构造了一个新的EKF包括方向四元数，非重力加速度，陀螺仪的偏差，和磁干扰的状态向量。然后，对后验误差协方差矩阵方程进行修正，建立REKF滤波器。我们比较了我们提出的REKF的准确性和鲁棒性与文献中使用最佳滤波器增益的四个滤波器。我们测量了9名参与者的大腿，小腿和脚的方向，同时使用MIMU和相机动作捕捉系统执行短期和长期任务。REKF在长期任务的准确性和鲁棒性方面显著优于文献中的过滤器（p < 0.05）。例如，对于足部MIMU，REKF和文献中的最佳滤波器的（侧倾、俯仰、偏航）的中值RMSE分别为（6.5，5.5，7.8）和（22.8，23.9，25）度。对于短期试验，与文献相比，REKF实现了显著（p < 0.05）更好或相似的性能。我们得出的结论是，包括非重力加速度，陀螺仪偏置，和磁干扰的状态向量，以及使用一个强大的滤波器结构，需要准确和强大的方向跟踪，至少在长时间的任务。
Accurate and robust orientation estimation using magnetic and inertial measurement units (MIMUs) has been a challenge for many years in long-duration measurements of joint angles and pedestrian dead-reckoning systems and has limited several real-world applications of MIMUs. Thus, this research aimed at developing a full-state Robust Extended Kalman Filter (REKF) for accurate and robust orientation tracking with MIMUs, particularly during long-duration dynamic tasks. First, we structured a novel EKF by including the orientation quaternion, non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector. Next, the a posteriori error covariance matrix equation was modified to build a REKF. We compared the accuracy and robustness of our proposed REKF with four filters from the literature using optimal filter gains. We measured the thigh, shank, and foot orientation of nine participants while performing short- and long-duration tasks using MIMUs and a camera motion-capture system. REKF outperformed the filters from literature significantly (p < 0.05) in terms of accuracy and robustness for long-duration tasks. For example, for foot MIMU, the median RMSE of (roll, pitch, yaw) were (6.5, 5.5, 7.8) and (22.8, 23.9, 25) deg for REKF and the best filter from the literature, respectively. For short-duration trials, REKF achieved significantly (p < 0.05) better or similar performance compared to the literature. We concluded that including non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector, as well as using a robust filter structure, is required for accurate and robust orientation tracking, at least in long-duration tasks.