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Nonlinear Filtering Under Stochastic Communication Protocol with Unknown Scheduling Probability

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  • Control Theory and Applications
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Abstract

In this paper, the UKF-type nonlinear filtering problem is investigated for general nonlinear systems under stochastic communication protocols (SCPs) with unknown scheduling probabilities. In order to avoid the data collision and alleviate the network communication burden, SCPs, allowed only one sensor node to send data via the shared network, are exploited to orchestrate the scheduling order of sensor nodes. Different from traditional assumptions with accurate statistics, the scheduling probability of the selected node is unknown, but lies in a reliable interval with known upper and lower bounds. Due to the unknown probabilities, the exact estimation error covariance is not available and hence its upper bound is derived with the help of adding zero terms and eigenvalues of positive definite matrices. Such an upper bound is dependent on known upper and lower bounds of the scheduling probabilities and further utilized to reasonably design the filter gain at each time instant. In light of the obtained covariance and the filter gain, an improved unscented transformation is developed to carry out the designed UKF-type nonlinear filter by improving traditional approximate mean and covariance. Furthermore, the impact of the uncertain size of unknown scheduling probabilities is thoroughly discussed. Finally, a numerical example is given to confirm the effectiveness of the proposed nonlinear filter.

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Author information

Authors and Affiliations

  1. College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Dan Liu & Guoliang Wei

  2. Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Derui Ding & Ying Sun

Authors
  1. Dan Liu
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  2. Derui Ding
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  3. Ying Sun
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  4. Guoliang Wei
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Corresponding author

Correspondence to Derui Ding.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported in part by the National Natural Science Foundation of China under Grants 61973219 and 61933007.

Dan Liu received her B.Sc. degree in mathematics and applied mathematics from Anyang Normal University, Anyang, China, in 2018. She is currently pursuing an M.Sc. degree from the College of Science, University of Shanghai for Science and Technology, Shanghai, China. Her research interests include nonlinear systems, Kalman filter, as well as sensor networks.

Derui Ding received both his B.Sc. degree in industry engineering, in 2004 and an M.Sc. degree in detection technology and automation equipment from Anhui Polytechnic University, Wuhu, China, in 2007, and a Ph.D. degree in control theory and control engineering from Donghua University, Shanghai, China, in 2014. From July 2007 to December 2014, he was a teaching assistant and then a lecturer in the Department of Mathematics, Anhui Polytechnic University, Wuhu, China. He is currently a Senior Research Fellow with the School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia. From June 2012 to September 2012, he was a research assistant in the Department of Mechanical Engineering, the University of Hong Kong, Hong Kong. From March 2013 to March 2014, he was a visiting scholar in the Department of Information Systems and Computing, Brunel University London, UK. His research interests include nonlinear stochastic control and filtering, as well as multi-agent systems and sensor networks. He has published around 80 papers in refereed international journals, and received The 2020 IEEE Systems, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, and the IET Control Theory and Applications Premium Award 2018. He is serving as an Associate Editor for Neurocomputing and IET Control Theory & Applications. He is also a very active reviewer for many international journals.

Ying Sun received her B.Sc. degree in physics from Harbin Normal University, Harbin, China, in 2013. She is currently pursuing a Ph.D. degree in control science and engineering from University of Shanghai for Science and Technology, Shanghai, China. She is an active reviewer for many international journals. Her current research interests include networked control systems, stochastic control and filtering as well as H∞, ℓ2-ℓ∞ control and filtering.

Guoliang Wei received his B.Sc. degree in mathematics from Henan Normal University, Xinxiang, China, in 1997 and an M.Sc. degree in applied mathematics and a Ph.D. degree in control engineering, both from Donghua University, Shanghai, China, in 2005 and 2008, respectively. He is currently a Professor with the Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China. From March 2010 to May 2011, he was an Alexander von Humboldt Research Fellow in the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Germany. From March 2009 to February 2010, he was a post doctoral research fellow in the Department of Information Systems and Computing, Brunel University, Uxbridge, UK, sponsored by the Leverhulme Trust of the UK From June to August 2007, he was a Research Assistant at the University of HongKong. From March to May 2008, he was a Research Assistant at the City University of Hong Kong. His research interests include nonlinear systems, stochastic systems, and bioinformatics. He has published over 100 papers in refereed international journals. His current research interests include nonlinear systems, stochastic systems, and bioinformatics. Dr. Wei is a very active reviewer for many international journals.

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Liu, D., Ding, D., Sun, Y. et al. Nonlinear Filtering Under Stochastic Communication Protocol with Unknown Scheduling Probability. Int. J. Control Autom. Syst. 19, 3343–3353 (2021). https://doi.org/10.1007/s12555-020-0337-5

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  • DOI: https://doi.org/10.1007/s12555-020-0337-5

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