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

• Published in: Journal of systems science and complexity Vol. 36; no. 4; pp. 1423 - 1442
• Main Authors: Hu, Jun, Hu, Zhibin, Dong, Hongli, Liu, Hongjian
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s11424-023-2183-z

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.