Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays: Optimized Algorithm Design and Boundedness Analysis

This paper is concerned with the distributed resilient fusion filtering (DRFF) problem for a class of time-varying multi-sensor nonlinear stochastic systems (MNSSs) with random sensor delays (RSDs). The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features....

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Vydáno v:	Journal of systems science and complexity Ročník 36; číslo 4; s. 1423 - 1442
Hlavní autoři:	Hu, Jun, Hu, Zhibin, Dong, Hongli, Liu, Hongjian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023 Springer Nature B.V
Témata:	Algorithms Complex Systems Control Design optimization Filtration Mathematics Mathematics and Statistics Mathematics of Computing Nonlinear systems Operations Research/Decision Theory Perturbation Random variables Sensors Statistics Stochastic systems Systems Theory Taylor series Upper bounds matrix-weighted fusion nonlinear time-varying systems random sensor delays Distributed resilient fusion filtering
ISSN:	1009-6124, 1559-7067
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Abstract	This paper is concerned with the distributed resilient fusion filtering (DRFF) problem for a class of time-varying multi-sensor nonlinear stochastic systems (MNSSs) with random sensor delays (RSDs). The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features. In addition, the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering problem. The aim of the addressed issue is to propose a DRFF scheme for MNSSs such that, for both RSDs and estimator gain perturbations, certain upper bounds of estimation error covariance (EEC) are given and locally minimized at every sample time. In the light of the obtained local filters, a new DRFF algorithm is developed via the matrix-weighted fusion method. Furthermore, a sufficient condition is presented, which can guarantee that the local upper bound of the EEC is bounded. Finally, a numerical example is provided, which can show the usefulness of the developed DRFF approach.
AbstractList	This paper is concerned with the distributed resilient fusion filtering (DRFF) problem for a class of time-varying multi-sensor nonlinear stochastic systems (MNSSs) with random sensor delays (RSDs). The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features. In addition, the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering problem. The aim of the addressed issue is to propose a DRFF scheme for MNSSs such that, for both RSDs and estimator gain perturbations, certain upper bounds of estimation error covariance (EEC) are given and locally minimized at every sample time. In the light of the obtained local filters, a new DRFF algorithm is developed via the matrix-weighted fusion method. Furthermore, a sufficient condition is presented, which can guarantee that the local upper bound of the EEC is bounded. Finally, a numerical example is provided, which can show the usefulness of the developed DRFF approach.
Author	Liu, Hongjian Hu, Jun Hu, Zhibin Dong, Hongli
Author_xml	– sequence: 1 givenname: Jun surname: Hu fullname: Hu, Jun email: jhu@hrbust.edu.cn organization: School of Automation, Harbin University of Science and Technology, Department of Mathematics, Harbin University of Science and Technology, School of Mathematics-Physics and Finance, Anhui Polytechnic University – sequence: 2 givenname: Zhibin surname: Hu fullname: Hu, Zhibin organization: Department of Mathematics, Harbin University of Science and Technology – sequence: 3 givenname: Hongli surname: Dong fullname: Dong, Hongli organization: Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems, Harbin University of Science and Technology – sequence: 4 givenname: Hongjian surname: Liu fullname: Liu, Hongjian organization: Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University
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Keywords	matrix-weighted fusion nonlinear time-varying systems random sensor delays Distributed resilient fusion filtering
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SubjectTerms	Algorithms Complex Systems Control Design optimization Filtration Mathematics Mathematics and Statistics Mathematics of Computing Nonlinear systems Operations Research/Decision Theory Perturbation Random variables Sensors Statistics Stochastic systems Systems Theory Taylor series Upper bounds
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Title	Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays: Optimized Algorithm Design and Boundedness Analysis
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