Reduced-order filtering for semi-Markovian jump systems against randomly occurring false data injection attacks

This paper describes a mode-dependent reduced-order filtering problem for semi-Markovian jump systems with time-varying delay and external disturbance, where the measurement output is vulnerable to randomly occurring false data injection attacks. To fa-cilitate analysis, the attacks are described by a nonlinear function that meets Lipschitz con-tinuity and the possible attack scenarios are represented by a stochastic parameter that fol-lows the Bernoulli distribution. Based on the information from the considered system and reduced-order filter, an augmented filtering system is constructed. Then, a convex opti-mization problem is formulated by using Lyapunov-Krasovskii stability theory and stochas-tic analysis. The filter gain matrices are efficiently derived as a result, ensuring that the augmented filtering system is stochastically stable and strictly (Q , S, R ) - gamma-dissipative. Through numerical examples, the advantages and effectiveness of the developed theoreti-cal findings are clearly demonstrated. (c) 2023 Elsevier Inc. All rights reserved.