Multi-rate strong tracking square-root cubature Kalman filter for MEMS-INS/GPS/polarization compass integrated navigation system

被引：85

作者：

Shen, Chong ^{[1
]}

Xiong, Yufeng ^{[1
]}

Zhao, Donghua ^{[1
]}

Wang, Chenguang ^{[1
]}

Cao, Huiliang ^{[1
]}

Song, Xiang ^{[2
]}

Tang, Jun ^{[1
]}

Liu, Jun ^{[1
]}

机构：

[1] North Univ China, Sch Instrument & Elect, Key Lab Instrumentat Sci & Dynam Measurement, Minist Educ, Taiyuan 030051, Peoples R China

[2] Nanjing Xiaozhuang Univ, Sch Elect Engn, Nanjing 211171, Peoples R China

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2022年 / 163卷

基金：

中国国家自然科学基金;

关键词：

Cubature Kalman filter; Strong tracking; Multi-rate residual; Integrated navigation system; SENSOR;

D O I：

10.1016/j.ymssp.2021.108146

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The microelectromechanical system-inertial navigation system (MEMS-INS) is one of the most widely used sensor systems in navigation but must be used with other sensors because of its error accumulation characteristics. The information fusion method for integrated navigation systems based on filtering technology is thus very important. This paper introduces a new adaptive Kalman filter for nonlinear integrated systems. An improved multi-rate strong tracking square-root cubature Kalman filter (MR-STSCKF) for a MEMS-INS/Global Positioning System (GPS)/polarization compass integrated navigation system is proposed. The proposed filter is used to estimate the system covariance adaptively. The proposed approach can overcome the problem of the inconsistency between the sampling frequencies of different sensors while maintaining the high precision of the integrated navigation results. Experimental results demonstrate that the proposed MR-STSCKF algorithm is effective in improving the accuracy of the MEMS-INS/GPS/polarization compass integrated navigation system with a high sampling frequency.

引用

页数：11

共 27 条

[1] Cubature Kalman Filters [J].

Arasaratnam, Ienkaran ;

Haykin, Simon .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2009, 54 (06) :1254-1269

[2] Estimation of longitudinal force, lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles [J].

Chen, Te ;

Xu, Xing ;

Chen, Long ;

Jiang, Haobing ;

Cai, Yingfeng ;

Li, Yong .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 101 :377-388

[3]

Feng K., 2018, SENSORS SWITZERLAND, V28, P1

[4] Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter [J].

Gao, Bingbing ;

Gao, Shesheng ;

Hu, Gaoge ;

Zhong, Yongmin ;

Gu, Chengfan .

AEROSPACE SCIENCE AND TECHNOLOGY, 2018, 73 :184-196

[5] A Modified Nonlinear Two-Filter Smoothing for High-Precision Airborne Integrated GPS and Inertial Navigation [J].

Gong, Xiaolin ;

Zhang, Jianxu ;

Fang, Jiancheng .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2015, 64 (12) :3315-3322

[6] The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution [J].

Groves, Paul D. .

JOURNAL OF NAVIGATION, 2014, 67 (02) :311-326

[7] Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system [J].

Hu, Gaoge ;

Gao, Bingbing ;

Zhong, Yongmin ;

Gu, Chengfan .

INFORMATION FUSION, 2020, 64 :194-204

[8] Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration [J].

Hu, Gaoge ;

Ni, Longqiang ;

Gao, Bingbing ;

Zhu, Xinhe ;

Wang, Wei ;

Zhong, Yongmin .

IEEE ACCESS, 2020, 8 :4814-4823

[9] A new direct filtering approach to INS/GNSS integration [J].

Hu, Gaoge ;

Wang, Wei ;

Zhong, Yongmin ;

Gao, Bingbing ;

Gu, Chengfan .

AEROSPACE SCIENCE AND TECHNOLOGY, 2018, 77 :755-764

[10] A derivative UKF for tightly coupled INS/GPS integrated navigation [J].

Hu, Gaoge ;

Gao, Shesheng ;

Zhong, Yongmin .

ISA TRANSACTIONS, 2015, 56 :135-144

← 1 2 3 →