Resilient Distributed Filtering for Discrete Time-varying Systems with Missing Measurements and Stochastic Uncertainties
In this paper, the resilient distributed filtering problem is addressed for discrete stochastic uncertain time-varying systems with missing measurements and stochastic uncertainties over wireless sensor networks. Some random variables governed by the Bernoulli distribution are used to model the miss...
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| Published in | International journal of control, automation, and systems Vol. 19; no. 5; pp. 1807 - 1818 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Bucheon / Seoul
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.05.2021
Springer Nature B.V 제어·로봇·시스템학회 |
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| Abstract | In this paper, the resilient distributed filtering problem is addressed for discrete stochastic uncertain time-varying systems with missing measurements and stochastic uncertainties over wireless sensor networks. Some random variables governed by the Bernoulli distribution are used to model the missing measurements phenomenon of each sensor node. In addition, the stochastic uncertainties are characterized by the multiplicative noises and stochastic nonlinearities. By using the variance-constrained method, an appropriate filter gain is selected to minimize the trace of the upper bound for the filter error covariance. Moreover, the resilient distributed filtering algorithm is designed and a new matrix simplification technique is introduced to deal with the sparsity of sensor networks topology. Finally, both the feasibility and effectiveness of the resilient distributed filtering algorithm are verified by a numerical simulation. |
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| AbstractList | In this paper, the resilient distributed filtering problem is addressed for discrete stochastic uncertain timevarying systems with missing measurements and stochastic uncertainties over wireless sensor networks. Some random variables governed by the Bernoulli distribution are used to model the missing measurements phenomenon of each sensor node. In addition, the stochastic uncertainties are characterized by the multiplicative noises and stochastic nonlinearities. By using the variance-constrained method, an appropriate filter gain is selected to minimize thetrace of the upper bound for the filter error covariance. Moreover, the resilient distributed filtering algorithm isdesigned and a new matrix simplification technique is introduced to deal with the sparsity of sensor networks topology. Finally, both the feasibility and effectiveness of the resilient distributed filtering algorithm are verified by anumerical simulation. KCI Citation Count: 3 In this paper, the resilient distributed filtering problem is addressed for discrete stochastic uncertain time-varying systems with missing measurements and stochastic uncertainties over wireless sensor networks. Some random variables governed by the Bernoulli distribution are used to model the missing measurements phenomenon of each sensor node. In addition, the stochastic uncertainties are characterized by the multiplicative noises and stochastic nonlinearities. By using the variance-constrained method, an appropriate filter gain is selected to minimize the trace of the upper bound for the filter error covariance. Moreover, the resilient distributed filtering algorithm is designed and a new matrix simplification technique is introduced to deal with the sparsity of sensor networks topology. Finally, both the feasibility and effectiveness of the resilient distributed filtering algorithm are verified by a numerical simulation. |
| Author | Liu, Tianqi Chen, Dongyan Hu, Jun |
| Author_xml | – sequence: 1 givenname: Tianqi surname: Liu fullname: Liu, Tianqi organization: Department of Mathematics, Harbin University of Science and Technology – sequence: 2 givenname: Dongyan orcidid: 0000-0001-9079-6900 surname: Chen fullname: Chen, Dongyan email: dychen_2004@hotmail.com organization: Department of Mathematics, Harbin University of Science and Technology – sequence: 3 givenname: Jun orcidid: 0000-0002-7852-5064 surname: Hu fullname: Hu, Jun email: hujun2013@gmail.com organization: Department of Mathematics, Harbin University of Science and Technology, School of Engineering, University of South Wales |
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| Cites_doi | 10.1016/j.comnet.2008.04.002 10.1016/j.inffus.2017.03.003 10.1049/iet-cta.2016.1096 10.1016/j.sigpro.2018.10.012 10.1016/j.automatica.2016.12.026 10.1080/00207721.2020.1765219 10.1016/0005-1098(93)90148-M 10.1109/JSEN.2016.2555761 10.1016/j.sigpro.2008.01.027 10.1109/TCSI.2019.2961804 10.1109/TSP.2012.2190599 10.1109/JSEN.2018.2859378 10.1109/TCSI.2017.2695481 10.1016/j.amc.2018.11.035 10.1109/TIM.2008.919009 10.1109/TSMC.2019.2905253 10.1080/00207721.2020.1746439 10.1109/TMC.2004.41 10.1109/JSEN.2012.2227995 10.1109/TCSI.2018.2878245 10.1109/TAC.2015.2390554 10.1109/TCSI.2018.2857771 10.1016/j.neucom.2018.07.086 10.1016/j.sigpro.2018.07.027 10.1016/j.sigpro.2018.09.036 10.1016/j.isatra.2018.10.045 10.1016/j.ins.2015.09.027 10.1109/MWC.2004.1368893 10.1016/j.jfranklin.2017.11.010 10.1109/TVT.2012.2190631 10.1109/TNNLS.2019.2927554 10.1080/00207721.2018.1542045 10.1109/TAC.2018.2876094 10.1109/TSP.2016.2634541 10.1016/j.amc.2019.124574 10.1109/TFUZZ.2020.3006572 10.1007/s11432-017-9256-3 |
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| Keywords | variance-constrained method resilient distributed filtering Missing measurements stochastic uncertainties sensor networks |
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| SubjectTerms | Algorithms Control Control Theory and Applications Discrete time systems Engineering Filtration Mathematical models Mechatronics Random variables Robotics Sensors Topology Uncertainty Upper bounds Wireless networks Wireless sensor networks 제어계측공학 |
| Title | Resilient Distributed Filtering for Discrete Time-varying Systems with Missing Measurements and Stochastic Uncertainties |
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