电子测量与仪器学报

马星河,毕文龙,朱行,于振子.改进 SHAKF 算法消除 IMU 随机误差的研究[J].电子测量与仪器学报,2021,35(12):59-67

改进 SHAKF 算法消除 IMU 随机误差的研究

Research on improving SHAKF algorithm to eliminate random error of IMU

DOI：

中文关键词: MEMS-IMU Allan 方差自适应鲁棒卡尔曼滤波随机误差 Sage-Husa 自适应卡尔曼滤波

英文关键词:micro-electromechanical inertial measurement unit Allan standard deviation adaptive robust Kalman filtering random error Sage-Husa adaptive Kalman filtering

基金项目:国家自然科学基金（182300410280）项目资助

作者	单位
马星河	1. 河南理工大学电气学院
毕文龙	1. 河南理工大学电气学院
朱行	1. 河南理工大学电气学院
于振子	2. 平煤神马集团煤炭开采利用研究院

Author	Institution
Ma Xinghe	1. School of Electrical Engineering, Henan Polytechnic University
Bi Wenlong	1. School of Electrical Engineering, Henan Polytechnic University
Zhu Hang	1. School of Electrical Engineering, Henan Polytechnic University
Yu Zhenzi	2. Research Institute of Coal Mining and Utilization, Pingmei Shenma Group

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中文摘要:

针对 Sage-Husa 自适应卡尔曼滤波(SHAKF)算法在处理惯性测量单元(IMU)时,随机误差容易随着时间的累积而造成滤波发散的问题,提出一种改进的 Sage-Husa 自适应鲁棒卡尔曼滤波 (MSHARKF)算法。首先对 IMU 构建了合适的模型,再将 SHAKF 与自适应鲁棒卡尔曼滤波(ARKF)相结合并纳入改进的时变噪声估计器,再引入最优自适应比例因子 αk 对量测方程迭代更新,最后得出新的预测协方差矩阵代入原方程。实验结果表明,分别通过 Allan 方差和均方根误差(RMSE),对 MEMS-IMU 滤波前后的静/ 动态数据分析计算得,随机误差噪声分别减小至原数据的 1 / 10 000 和 1 / 100。与本文其他算法相比,该方法有效地对算法滤波发散进行了抑制,进而提高了 IMU 的测量精度和长期稳定性。

英文摘要:

Aiming at the problem that when the Sage-Husa adaptive Kalman filter ( SHAKF ) algorithm is processing inertial measurement units ( IMU), random errors are likely to accumulate over time and cause filter divergence, an improved Sage-Husa adaptive robustness Kalman filter (MSHARKF) algorithm is proposed. First, build a suitable model for IMU, then combine SHAKF with adaptive robust Kalman filter (ARKF) and incorporate it into the improved time-varying noise estimator, and then introduce the optimal adaptive scale factor (αk ) to the measurement equation Iteratively update, and finally get a new predicted covariance matrix to be substituted into the original equation. The experimental results show that through the Allan variance and root mean square error (RMSE), the static / dynamic data before and after the MEMS-IMU filtering is calculated and the random error noise is reduced to one ten thousandth and one percent of the original data, respectively. Compared with other algorithms in this paper, this method effectively suppresses the filtering divergence of the algorithm, thereby improving the measurement accuracy and long-term stability of the IMU.

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