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基于非正交MEMS惯性传感器阵列的MIMU最优冗余结构与信号融合算法

MIMU Optimal Redundant Structure and Signal Fusion Algorithm Based on a Non-Orthogonal MEMS Inertial Sensor Array.

作者信息

Xue Liang, Yang Bo, Wang Xinguo, Cai Guangbin, Shan Bin, Chang Honglong

机构信息

Department of Control Engineering, Xi'an Research Institute of High Technology, Hongqing Town, No. 2 Tongxin Road, Xi'an 710025, China.

Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, No. 127 Youyi West Road, Xi'an 710072, China.

出版信息

Micromachines (Basel). 2023 Mar 29;14(4):759. doi: 10.3390/mi14040759.

DOI:10.3390/mi14040759
PMID:37420992
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10145135/
Abstract

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU's accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU's performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro's error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro's ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of , and were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.

摘要

微惯性测量单元（MIMU）通常用于感知飞行载体的角速率和加速度。在本研究中，使用多个MEMS陀螺仪形成空间非正交阵列以构建冗余MIMU系统，并通过稳态卡尔曼滤波器（KF）增益建立最优卡尔曼滤波（KF）算法来组合阵列信号，以提高MIMU的精度。利用噪声相关性优化非正交阵列的几何布局，并揭示相关性和几何布局对MIMU性能提升的影响机制。此外，针对4、5、6、8陀螺仪的非正交阵列设计并分析了两种不同的锥形配置结构。最后，设计了一个冗余4-MIMU系统来验证所提出的结构和KF算法。结果表明，通过非正交阵列融合可以准确估计输入信号速率，并且还可以有效降低陀螺仪的误差。4-MIMU系统的结果表明，陀螺仪的角随机游走（ARW）和速率随机游走（RRW）噪声可分别降低约3.5倍和2.5倍。特别是，在x、y和z轴上的估计误差（1σ）分别比单个陀螺仪低4.9倍、4.6倍和2.9倍。

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