组网冗余MEMS惯性传感器网络优化配置与融合处理方法

doi:10.3969/j.issn.1001-506X.2020.11.22

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (11): 2591-2599.doi: 10.3969/j.issn.1001-506X.2020.11.22

组网冗余MEMS惯性传感器网络优化配置与融合处理方法

马龙¹(), 刘宇哲¹(), 代超璠²(), 周航¹(), 孙凤鸣¹()

1. 中国民航大学中欧航空工程师学院, 天津 300300
2. 中国商用飞机有限责任公司北京民用飞机技术研究中心, 北京 102211

收稿日期:2020-02-13 出版日期:2020-11-01 发布日期:2020-11-05
作者简介:马龙(1983-),男,副教授,硕士研究生导师,博士,主要研究方向为精密仪器设计与无损检测。E-mail:longma@cauc.edu.cn|刘宇哲(1996-),男,硕士研究生,主要研究方向为精密仪器设计、无损检测与计算机视觉。E-mail:liuyuzhe_1996@163.com|代超璠(1994-),男,硕士,主要研究方向为MEMS传感器与惯性元器件噪声。E-mail:daichaofan@comac.cc|周航(1990-),男,讲师,博士,主要研究方向为电波传输,微波与数值模拟方法。E-mail:enzo-zhouhang@hotmail.com|孙凤鸣(1983-),男,讲师,硕士研究生导师,博士,主要研究方向为MEMS传感器与微纳米测量。E-mail:reconi@163.com
基金资助:
国家自然科学基金委员会与中国民用航空局联合资助项目(U1633101);中央高校基本科研业务费中国民航大学专项(3122018Z002)

Networking redundant MEMS inertial sensor network optimal configuration and fusion processing method

Long MA¹(), Yuzhe LIU¹(), Chaofan DAI²(), Hang ZHOU¹(), Fengming SUN¹()

1. Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China
2. Beijing Aeronautical Science & Technology Research Institute, Commercial Aircraft Corporation of China, Beijing 102211, China

Received:2020-02-13 Online:2020-11-01 Published:2020-11-05

摘要/Abstract

摘要：

针对单个惯性传感器精度与可靠性问题,提出了一种组网冗余微机电系统(micro-electro-mechanical system, MEMS)惯性传感网络优化配置与融合处理方法。首先,研究了多惯性传感器节点的空间配置形式,给出了网络观测模型。利用节点之间的最优化冗余配置策略,提升系统测量可靠性。在此基础上,进一步考虑MEMS陀螺仪和加速度计的安装误差对输出结果的影响,提出了相应的误差模型及校正算法。以随机游走为主要误差源,设计了新型的卡尔曼滤波方法,实现了冗余配置下的高精度导航信息解算。最后,采用自主构建的实验系统进行试验,证明所提出的融合方法能够有效降低陀螺仪和加速度计的随机游走误差。车载试验进一步证明了所提出的方法及系统的有效性。

关键词: 惯性传感网络, 最优配置, 冗余结构, 误差模型, 卡尔曼滤波

Abstract:

To improve the precision and reliability of the single inertial sensor, an optimal configuration and fusion processing method of inertial sensing network for the network redundant micro-electro-mechanical system (MEMS) is proposed. Firstly, the spatial configuration of multi-inertial sensor nodes is investigated, and the network observation model is proposed. By utilizing the optimal redundancy configuration, the reliability of the system is improved. On the basis, the impacts of the installation errors for the MEMS gyroscope and the accelerometer on output are further considered, the error model and the correction algorithm are proposed. By setting the random walk as the major error source, a novel Kalman filter method is designed, and the navigation information with high precision is calculated under the condition of redundancy configuration. The experiments on a self-built system show that the proposed fusion method can substantially reduce the random walk errors of the gyroscope and the accelerometer. The on-vehicle test further verifies the effectiveness of the proposed method and system.

Key words: inertial sensing network, optimal configuration, redundant structure, error model, Kalman filter

中图分类号:

马龙, 刘宇哲, 代超璠, 周航, 孙凤鸣. 组网冗余MEMS惯性传感器网络优化配置与融合处理方法[J]. 系统工程与电子技术, 2020, 42(11): 2591-2599.

Long MA, Yuzhe LIU, Chaofan DAI, Hang ZHOU, Fengming SUN. Networking redundant MEMS inertial sensor network optimal configuration and fusion processing method[J]. Systems Engineering and Electronics, 2020, 42(11): 2591-2599.

图/表 22

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参考文献 30

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敏感轴	x₁	y₁	z₁	x₂	y₂	z₂	x₃	y₃	z₃
角速度随机游走噪声系数/(10^-2(°)/h^3/2)	8.8	6.4	7.9	5.7	5.7	8.5	5.5	8.7	6.9
加速度随机游走噪声系数/(10^-4 m/h^3/2)	4.2	3.9	4.3	4.1	5.0	4.6	5.0	4.4	5.1

敏感轴	X	Y	Z
角速度随机游走噪声系数/(10^-3(°)/h^3/2)	6.3	7.1	6.8
加速度随机游走噪声系数/(10^-5 m/h^3/2)	0.98	1.1	1.3

敏感轴	X	Y	Z
角速度随机游走噪声系数/(10^-3(°)/h^3/2)	13	9.3	11
加速度随机游走噪声系数/(10^-5 m/h^3/2)	2.0	2.9	2.4

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组网冗余MEMS惯性传感器网络优化配置与融合处理方法

Networking redundant MEMS inertial sensor network optimal configuration and fusion processing method

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