Chen Jiaxuan, Cui Bingbo, Wei Xinhua, Zhu Yongyun, Sun Zeyu, Liu Yufei
Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China.
School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2024 Jul 17;24(14):4623. doi: 10.3390/s24144623.
Attitude determination based on a micro-electro-mechanical system inertial measurement unit (MEMS-IMU) has attracted extensive attention. The non-gravitational components of the MEMS-IMU have a significant effect on the accuracy of attitude estimation. To improve the attitude estimation of low-dynamic vehicles under uneven soil conditions or vibrations, a robust Kalman filter (RKF) was developed and tested in this paper, where the noise covariance was adaptively changed to compensate for the external acceleration of the vehicle. The state model for MEMS-IMU attitude estimation was initially constructed using a simplified direction cosine matrix. Subsequently, the variance of unmodeled external acceleration was estimated online based on filtering innovations of different window lengths, where the acceleration disturbance was addressed by tradeoffs in time-delay and prescribed computation cost. The effectiveness of the RKF was validated through experiments using a three-axis turntable, an automatic vehicle, and a tractor tillage test. The turntable experiment demonstrated that the angle result of the RKF was 0.051° in terms of root mean square error (RMSE), showing improvements of 65.5% and 29.2% over a conventional KF and MTi-300, respectively. The dynamic attitude estimation of the automatic vehicle showed that the RKF achieves smoother pitch angles than the KF when the vehicle passes over speed bumps at different speeds; the RMSE of pitch was reduced from 0.875° to 0.460° and presented a similar attitude trend to the MTi-300. The tractor tillage test indicated that the RMSE of plough pitch was improved from 0.493° with the KF to 0.259° with the RKF, an enhancement of approximately 47.5%, illustrating the superiority of the RKF in suppressing the external acceleration disturbances of IMU-based attitude estimation.
基于微机电系统惯性测量单元（MEMS - IMU）的姿态确定已引起广泛关注。MEMS - IMU的非引力分量对姿态估计精度有显著影响。为提高低动态车辆在不平坦土壤条件或振动下的姿态估计，本文开发并测试了一种鲁棒卡尔曼滤波器（RKF），其中噪声协方差被自适应改变以补偿车辆的外部加速度。MEMS - IMU姿态估计的状态模型最初使用简化的方向余弦矩阵构建。随后，基于不同窗口长度的滤波创新在线估计未建模外部加速度的方差，其中通过权衡时间延迟和规定的计算成本来处理加速度干扰。通过使用三轴转台、自动车辆和拖拉机耕作试验的实验验证了RKF的有效性。转台实验表明，RKF的角度结果在均方根误差（RMSE）方面为0.051°，分别比传统卡尔曼滤波器和MTi - 300提高了65.5%和29.2%。自动车辆的动态姿态估计表明，当车辆以不同速度通过减速带时，RKF比卡尔曼滤波器实现了更平滑的俯仰角；俯仰角的RMSE从0.875°降低到0.460°，并且呈现出与MTi - 300相似的姿态趋势。拖拉机耕作试验表明，犁的俯仰角RMSE从卡尔曼滤波器的0.493°提高到RKF的0.259°，提高了约47.5%，说明了RKF在抑制基于IMU的姿态估计的外部加速度干扰方面的优越性。
