Probability-Guaranteed Distributed Filtering for Nonlinear Systems on Basis of Nonuniform Samplings Subject to Envelope Constraints

• Published in: IEEE transactions on signal and information processing over networks Vol. 10; pp. 905 - 915
• Main Authors: Wang, Wei, Hu, Chen, Ma, Lifeng, Yi, Xiaojian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convexity; distributed filtering; Envelope-constrained in probability; Filtration; Information processing; Markov chains; Matrix converters; multiplicative noise; Noise; Nonlinear systems; Nonuniform sampling; sensor networks; Sensors; State estimation; Symmetric matrices; Time measurement; Time-varying systems; Vectors
• ISSN: 2373-776X; 2373-7778
• DOI: 10.1109/TSIPN.2024.3496254

This paper investigates the probability-guaranteed distributed <inline-formula><tex-math notation="LaTeX">H_\infty</tex-math></inline-formula> filtering problem for stochastic time-varying systems over sensor networks. The measurements from sensing nodes are sampled nonuniformly before being received by filters and the sampling processes are modeled by a set of Markov chains. The purpose of the addressed problem is to design a distributed filter algorithm which meets the finite-horizon average <inline-formula><tex-math notation="LaTeX">H_\infty</tex-math></inline-formula> performance, meanwhile guaranteeing all filtering errors bounded within a prespecified envelope with a certain probability. Sufficient conditions for the feasibility of the mentioned filtering technique are established using convex optimization techniques. The desired filtering gains are subsequently determined by resolving the relevant matrix inequalities at each time step. Finally, the effectiveness of the proposed filtering algorithm is shown via an illustrative numerical example.