Self-Triggered Asynchronous Estimation for Nonlinear Markov Systems Under Stochastic Hybrid Attacks

This article focuses on the self-triggered estimator for nonlinear Markov jump systems (MJSs) subject to stochastic hybrid attacks, including deception attacks (DAs) and denial-of-service (DoS) attacks. To alleviate communication pressure and eliminate the need for continuous detection, a dynamic self-triggered mechanism (DSTM) is proposed, wherein the dynamic variable is adaptively adjusted to conserve network resources more efficiently. Considering the inherent openness of communication networks and their susceptibility to attacks, two Markov chains are employed to characterize the random switching between different attack strategies. With the help of a mapping technique, the system modes and attack modes are integrated into a new Markov chain. In addition, a hidden Markov model (HMM) with incomplete transmission probabilities is considered to represent the mismatched modes and the covert characteristic of attacks. Then, sufficient conditions ensuring system stability are derived, and a dissipative estimator is developed. Finally, two examples are utilized to illustrate the efficacy of the derived theoretical results.