A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

doi:10.23919/JSEE.2024.000060

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (3): 732-740.doi: 10.23919/JSEE.2024.000060

• CONTROL THEORY AND APPLICATION • Previous Articles

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

Xu LYU¹(), Ziyang MENG¹(), Chunyu LI¹(), Zhenyu CAI²^,*(), Yi HUANG³(), Xiaoyong LI³(), Xingkai YU⁴()

¹ Department of Precision Instrument, Tsinghua University, Beijing 100084, China
² College of Mechanical and Power Engineering, Three Gorges University, Yichang 443002, China
³ Unit 91001 of the PLA, Beijing 100161, China
⁴ School of Control and Computer Engineering, North China Electric Power University, Beijing 100096, China

Received:2023-12-14 Online:2024-06-18 Published:2024-06-19
Contact: Zhenyu CAI E-mail:lvlay@163.com;ziyangmeng@tsinghua.edu.cn;lcyfly1@163.com;592178236@qq.com;lx_bird@126.com;2016301610349@whu.edu.cn;yuxingkai2007@163.com
About author:
LYU Xu was born in 1990. He received his B.S. and M.S. degrees in control theory and control engineering from the Department of Electrical Engineering, Liaoning University of Technology, Jinzhou, China, in 2014 and 2019, respectively, and Ph.D. degree in navigation, guidance, and control from the Department of Navigation Engineering, Naval University of Engineering, Wuhan, China, in 2022. He is currently a postdoctoral fellow with the Department of Precision Instrument, Tsinghua University, Beijing, China. His main research interests include inertial navigation systems, integrated navigation, and predictive control. E-mail: lvlay@163.com

MENG Ziyang was born in 1984. He received his B.S. degree from Huazhong University of Science and Technology, Wuhan, China in 2006, and Ph.D. degree from Tsinghua University, Beijing, China, in 2010. He was an Exchange Ph.D. student with Utah State University, Logan, UT, USA, from 2008 to 2009. He is currently an associate professor with the Department of Precision Instrument, Tsinghua University. Prior to joining Tsinghua University, he was a post doctoral researcher, a researcher, and a humboldt research fellow with Shanghai Jiao Tong University, Shanghai, China, the KTH Royal Institute of Technology, Stockholm, Sweden, and the Technical University of Munich, Munich, Germany, respectively, from 2010 to 2015. His research interests include distributed control and optimization and intelligent navigation technique.E-mail: ziyangmeng@tsinghua.edu.cn

LI Chunyu was born in 1992. He received his B.S. degree in vehicle engineering from Hunan University, Changsha, China, in 2015, M.S. degree in automotive engineering from University of Bath, Bath, U.K., in 2016, and Ph.D. degree in aeronautical and astronautical science and technology from Beijing Institute of Technology, Beijing, China, in 2023. He is currently a post-doctoral researcher with the Department of Precision Instrument, Tsinghua University, Beijing, China. His main research interests include distributed state estimation and visual-inertial navigation systems. E-mail: lcyfly1@163.com

CAI Zhenyu was born in 1998. He received his B.S. degree in engineering from the School of Mechanical Engineering, Wuhan Polytechnic University in 2020. Currently he is studying for his M.S. degree in the School of Mechanical and Power School, the China Three Gorges University. His research interests include inertial navigation and motion attitude control. E-mail: 592178236@qq.com

HUANG Yi was born in 1985. He received his B.S. degree in remote sensing information from the Information Engineering University in 2007, M.S. degree in geographic information systems from China University of Petroleum (Beijing) in 2018. He is now an engineer with Unit 91001 of the PLA. His research interests include geographic information systems and ship navigation. E-mail: lx_bird@126.com

LI Xiaoyong was born in 1997. He received his B.S. degree in surveying and mapping engineering from Wuhan University, Wuhan, China, in 2020, M.S. degree in control engineering and electronic information from Naval University of Engineering, Wuhan, China, in 2022. He is now an engineer with Unit 91001 of the PLA. His main research interests include geographic information systems and ship navigation. E-mail: 2016301610349@whu.edu.cn

YU Xingkai was born in 1988. He received his Ph.D. degree in control science and engineering from Shanghai Jiao Tong University in 2019. From 2019 to 2022, he was a post-doctoral fellow with the Department of Precision Instrument, Tsinghua University, Beijing, China. He is currently a distinguished associate professor with the School of Control and Computer Engineering, North China Electric Power University, Beijing, China. His main research interests include distributed state estimation, information fusion, system identification, and their applications in navigation technology and target tracking. E-mail: yuxingkai2007@163.com
Supported by:
This work was supported by China Postdoctoral Science Foundation (2023M741882) and the National Natural Science Foundation of China (62103222; 62273195).

Abstract

Abstract:

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF) master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

Key words: Kalman filter, dual-adaptive, integrated navigation, unscented Kalman filter (UKF), robust

Xu LYU, Ziyang MENG, Chunyu LI, Zhenyu CAI, Yi HUANG, Xiaoyong LI, Xingkai YU. A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system[J]. Journal of Systems Engineering and Electronics, 2024, 35(3): 732-740.

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References 24

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Quantity	Gyro	Accelerometer
Dynamic range	$ \pm {400^{{\circ }}}/{\text{s}} $	$ \pm 10g $
Bias	$ \leqslant {0.5^{{\circ }}}/{\text{h}} $	$ \leqslant 0.05{\text{ mg}} $
Update rate	$ 125{\text{ Hz}} $	$ 125{\text{ Hz}} $

Average estimation error	UKF	SH-AUKF	Dual-AUKF
Pitch error/(°)	0.2835	0.2682	0.2596
Roll error/(°)	0.3940	0.3965	0.3762
Yaw error/(°)	1.2798	1.2079	1.0744
East velocity error/(m·s⁻¹)	0.0207	0.0312	0.0034
North velocity error/(m·s⁻¹)	0.0323	0.0078	−0.0221
Latitude error/m	33.0567	−5.6838	−35.8853
Latigude error/m	37.8510	42.4647	−5.3834

Quantity	Gyro	Accelerometer
Dynamic range	$ \pm {400^{{\circ }}}/{\text{s}} $	$ \pm 10g $
Bias	$ \leqslant {0.003^{{\circ }}}/{\text{h}} $	$ \leqslant 5{\text{ mg}} $
Update rate	$ 200{\text{ Hz}} $	$ 200{\text{ Hz}} $

Quantity	DVL
Speed range/kn	$ - 10\;{\mathrm{to}}\;10$
Bias/(cm/s)	$ 0.3\% \pm 0.3$
Update rate/Hz	$ 1 $
Depth of the botton track/m	$ 80 $

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A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

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