Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

Jiaxing Li; Zidong Wang; Jun Hu; Hongli Dong; Hongjian Liu

IEEE/CAA Journal of Automatica Sinica

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J. Li, Z. Wang, J. Hu, H. Dong, and H. Liu, “Cubature Kalman fusion filtering under amplify-and-forward relays with randomly varying channel parameters,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–13, Jun. 2024.

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J. Li, Z. Wang, J. Hu, H. Dong, and H. Liu, “Cubature Kalman fusion filtering under amplify-and-forward relays with randomly varying channel parameters,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–13, Jun. 2024.

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

Funds: This work was supported in part by the National Natural Science Foundation of China (12171124, 61933007), the Natural Science Foundation of Heilongjiang Province of China (ZD2022F003), the National High-End Foreign Experts Recruitment Plan of China (G2023012004L), the Royal Society of UK, and the Alexander von Humboldt Foundation of Germany

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Author Bio:
Jiaxing Li received the B.Sc. degree in mathematics and applied mathematics from Harbin University of Commerce in 2017 and the M.Sc. degree in mathematics from Harbin University of Science and Technology in 2020, respectively. She is currently a Ph.D. candidate in mathematics at Harbin University of Science and Technology. From March 2023 to March 2024, she was a Visiting Ph.D. Student with the Department of Computer Science, Brunel University London, UK. Her current research interests include optimal distributed filtering for time-varying state-saturated systems, optimal distributed filtering for sensor networks, and cubature Kalman filtering. She is an active reviewer for many international journals

Zidong Wang (Fellow, IEEE) received the B.Sc. degree in mathematics in 1986 from Suzhou University, the M.Sc. degree in applied mathematics and the Ph.D. degree in electrical engineering both from Nanjing University of Science and Technology, in 1990 and 1994, respectively. He is currently a professor of dynamical systems and computing in the Department of Computer Science, Brunel University London, UK. From 1990 to 2002, he held teaching and research appointments in universities in China, Germany, and the UK. His research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published a number of papers in international journals. Prof. Wang is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, and the William Mong Visiting Research Fellowship of Hong Kong. He serves (or has served) as the Editor-in-Chief for International Journal of Systems Science, the Editor-in-Chief for Neurocomputing, the Editor-in-Chief for Systems Science & Control Engineering, and an Associate Editor for 12 international journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Neural Networks, IEEE Transactions on Signal Processing, and IEEE Transactions on Systems, Man, and Cybernetics–part C. He is a Member of the Academia Europaea, a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of the IEEE, a Fellow of the Royal Statistical Society, and a member of program committee for many international conferences

Jun Hu (Member, IEEE) received the B.Sc. degree in information and computing science and the M.Sc. degree in applied mathematics from Harbin University of Science and Technology, in 2006 and 2009, respectively, and the Ph.D. degree in control science and engineering from Harbin Institute of Technology in 2013. From September 2010 to September 2012, he was a Visiting Ph.D. Student in the Department of Information Systems and Computing, Brunel University London, Uxbridge, UK. From May 2014 to April 2016, he was an Alexander von Humboldt research fellow at the University of Kaiserslautern, Germany. From January 2018 to January 2021, he was a Research Fellow at University of South Wales, Pontypridd, UK. He is with the Department of Applied Mathematics, Harbin University of Science and Technology, and is also with the School of Automation, Harbin University of Science and Technology. His research interests include nonlinear control, filtering and fault estimation, time-varying systems and complex networks. Dr. Hu has published more than 90 papers in refereed international journals. He serves as a reviewer for Mathematical Reviews, as an Editor for Neurocomputing, Journal of Intelligent & Fuzzy Systems, Neural Processing Letters, Systems Science & Control Engineering, and as a Guest Editor for International Journal of General Systems and Information Fusion

Hongli Dong (Senior Member, IEEE) received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology in 2012. From 2009 to 2010, she was a Research Assistant with the Department of Applied Mathematics, City University of Hong Kong, China. From 2010 to 2011, she was a Research Assistant with the Department of Mechanical Engineering, The University of Hong Kong, China. From 2011 to 2012, she was a Visiting Scholar with the Department of Information Systems and Computing, Brunel University London, UK. From 2012 to 2014, she was an Alexander von Humboldt Research Fellow with the University of Duisburg−Essen, Germany. She is currently a Professor with the Artificial Intelligence Energy Research Institute, Northeast Petroleum University. She is also the Director of the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control. Her current research interests include robust control and networked control systems. She is a very active reviewer for many international journals

Hongjian Liu received the B.Sc. degree in applied mathematics in 2003 from Anhui University, and the M.Sc. degree in detection technology and automation equipment in 2009 from Anhui Polytechnic University, and the Ph.D. degree in control science and engineering in 2018 from Donghua University. In 2016, he was a Research Assistant with the Department of Mathematics, Texas A&M University at Qatar, Qatar, for two months. From March 2017 to March 2018, he was a Visiting Scholar in the Department of Information Systems and Computing, Brunel University London, UK. He is currently a Professor in the School of Mathematics and Physics, Anhui Polytechnic University. His current research interests include filtering theory, memristive neural networks, and network communication systems. He is a very active reviewer for many international journals
Corresponding author: Jun Hu, e-mail: jhu@hrbust.edu.cn
Received Date: 2024-02-06
Revised Date: 2024-04-12
Accepted Date: 2024-05-30

Available Online: 2024-11-07

Abstract

Abstract

In this paper, the problem of cubature Kalman fusion filtering (CKFF) is addressed for multi-sensor systems under amplify-and-forward (AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance’s upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
- Amplify-and-forward (AaF) relays,
- covariance intersection fusion,
- cubature Kalman filtering,
- multi-sensor systems,
- uniform boundedness

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References

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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