Distributed resilient fusion filtering for nonlinear systems with random sensor delay under round-robin protocol

Abstract

In this paper, we address the distributed resilient fusion filtering (DRFF) problem for a class of nonlinear multi-sensor networked systems (MSNSs) with random sensor delay (RSD) under round-robin protocol (RRP). The RSD is depicted by the Bernoulli random variable with known occurrence probability. In order to relieve the network congestion, the RRP that can deal with information overload issue of the transmission process from sensor to the estimator is utilised. The major objective of this paper is that the resilient fusion filter is designed for nonlinear MSNSs with RSD and RRP in the light of the inverse covariance intersection approach, where the local upper bound regarding the filtering error covariance is obtained and then minimised by suitably exploiting the local filter gain. Finally, a simulation example that can show the validity of the provided DRFF algorithm is presented.

Keywords:

• Distributed resilient fusion filtering
• nonlinear multi-sensor networked systems
• round-robin protocol
• inverse covariance intersection approach

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 12171124], the National College Students' Innovative Entrepreneurial Training Plan Program of China [grant number 202110214024], Talent Training Project of Reform and Development Foundation for Local Universities from Central Government of China: Youth Talent Project, and the Alexander von Humboldt Foundation of Germany.

Notes on contributors

Zhibin Hu

Zhibin Hu received the B.Sc. degree in Applied Mathematics from Baotou Teachers' College, Inner Mongolia, China, in 2015, the M.Sc. degree in Mathematics from the Harbin University of Science and Technology, Harbin, China, in 2019. He is currently pursuing a Ph.D. degree in Mathematics from Harbin University of Science and Technology, Harbin, China. His current research interests include state estimation and information fusion filtering.

Jun Hu

Jun Hu received a B.Sc. degree in Information and Computation Science and M.Sc. degree in Applied Mathematics from Harbin University of Science and Technology, Harbin, China, in 2006 and 2009, respectively, and the Ph.D. degree in Control Science and Engineering from Harbin Institute of Technology, Harbin, China, 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, UK. From May 2014 to April 2016, he was an Alexander von Humboldt research fellow at the University of Kaiserslautern, Kaiserslautern, Germany. From January 2018 to January 2021, he was a research fellow at the University of South Wales, Cardiff, UK. He is a Professor and Ph.D. supervisor in the Department of Mathematics, Harbin University of Science and Technology, Harbin 150080, China. His research interests include nonlinear control, filtering and fault estimation, time-varying systems and complex networks. He has published more than 80 papers in refereed international journals. Prof. Hu serves as a reviewer for Mathematical Reviews, as an editor for Neurocomputing, Journal of Intelligent and Fuzzy Systems, Neural Processing Letters, Systems Science and Control Engineering, and as a guest editor for the International Journal of General Systems and Information Fusion.

Hailong Tan

Hailong Tan received a B.Sc. degree in Statistics in 2014 and a Ph.D. degree in Control Theory and Control Engineering in 2020, both from Donghua University, Shanghai, China. He is currently a Lecturer in the School of Mathematics-Physics and Finance, Anhui Polytechnic University, Wuhu, China. From 2017 to 2018, he was a visiting Ph.D. student with the Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK. His current research interests include robust filtering, networked control systems and sampled-data systems.

Jinpeng Huang

Jinpeng Huang is currently working toward a B.Sc. degree in Information and Computing Science from Harbin University of Science and Technology, Harbin, China. His current research interests include intelligent algorithm analysis and design for complex dynamical systems.

Zhipeng Cao

Zhipeng Cao is currently working toward a B.Sc. degree in Information and Computing Science from Harbin University of Science and Technology, Harbin, China. His current research interests include intelligent algorithm analysis and design for complex dynamical systems.