A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

Fangfang Zhao; Cuiqiao Chen; Wei He; Shuzhi Sam Ge

doi:10.1109/JAS.2017.7510445

Volume 5 Issue 6

Nov. 2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(6): 1113-1120

Fangfang Zhao, Cuiqiao Chen, Wei He and Shuzhi Sam Ge, "A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1113-1120, Nov. 2018. doi: 10.1109/JAS.2017.7510445

Citation:

Fangfang Zhao, Cuiqiao Chen, Wei He and Shuzhi Sam Ge, "A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1113-1120, Nov. 2018. doi: 10.1109/JAS.2017.7510445

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A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

doi: 10.1109/JAS.2017.7510445

1.
School of Computer Science and Engineering, and Center for Robotics, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
School of Automation and Electrical Engineering, University of Science and Technology of Beijing, Beijing 100083, China
3.
Social Robotics Laboratory, Interactive Digital Media Institute, and the Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore

Funds:

the National Basic Research Program of China (973 Program) 2014CB744206

More Information

Author Bio:
Fangfang Zhao is a Ph.D. candidate at the School of Computer Science and Engineering, Center for Robotics, University of Electronic Science and Technology of China. Her research interests include computer science and technology, information fusion. (e-mail: fangfangzhao.uestc@gmail.com)

Cuiqiao Chen is a masters student at the School of Computer Science and Engineering, Center for Robotics, University of Electronic Science and Technology of China. Her main research interest is computer science. (e-mail: cqchen2018@163.com)

Wei He (S'09-M'12-SM'16) received his B.Eng. and his M.Eng. degrees from the College of Automation Science and Engineering, South China University of Technology (SCUT), China, in 2006 and 2008, respectively, and his Ph.D. degree from the Department of Electrical & Computer Engineering, the National University of Singapore (NUS), Singapore, in 2011. He is currently working as a Full Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. He has been awarded a Newton Advanced Fellowship from the Royal Society, UK. He is a recipient of the IEEE SMC Society Andrew P. Sage Best Transactions Paper Award in 2017. He serves as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man, and Cybernetics:Systems, IEEE Access, and IEEE/CAA Journal of Automatica Sinica, and an Editor of Journal of Intelligent & Robotic Systems. He is the member of the IFAC TC on Distributed Parameter Systems, IFAC TC on Computational Intelligence in Control and IEEE CSS TC on Distributed Parameter Systems. His current research interests include robotics, distributed parameter systems and intelligent control systems.(email: weihe@ieee.org)

Shuzhi Sam Ge (S'90-M'92-SM'00-F'06) received his Ph.D. degree from the Imperial College London, London, UK, in 1993, and his B.Sc. degree from the Beijing University of Aeronautics and Astronautics, Beijing, China, in 1986.
He is the Director of the Social Robotics Laboratory of Interactive Digital Media Institute, Singapore, and the Centre for Robotics, Chengdu, China, and a Professor with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, on leave from the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu. He has co-authored four books and over 300 international journal and conference papers. His current research interests include social robotics, adaptive control, intelligent systems, and artificial intelligence.
Dr. Ge is a Editor-in-Chief of the International Journal of Social Robotics (Springer). He has served/been serving as an Associate Editor for a number of flagship journals, including the IEEE Transactions on Automatic Control, the IEEE Transactions on Control Systems Technology, the IEEE Transactions On Neural Networks, and Automatica. He serves as a Book Editor for the Taylor and Francis Automation and Control Engineering Series. He served as the Vice President for Technical Activities, from 2009 to 2010, the Vice President of Membership Activities, from 2011 to 2012, and a member of the Board of Governors, from 2007 to 2009 at the IEEE Control Systems Society. He is a Fellow of the International Federation of Automatic Control, the Institution of Engineering and Technology, and the Society of Automotive Engineering. (e-mail: samge@nus.edu.sg)
Corresponding author: Fangfang Zhao, e-mail: fangfangzhao.uestc@gmail.com
Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.
Received Date: 2015-10-21
Accepted Date: 2016-09-20

Abstract

Abstract

This paper explores multiple model adaptive estimation (MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter-multiple model adaptive estimation unscented Kalman filter (MMAE-UKF) rather than conventional Kalman filter methods, like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system (SINS) and celestial navigation system (CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

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References(22)

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A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

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