Distributed $$H_{\infty }$$-Consensus Filtering over Sensor Networks Under Deception Attacks

Abstract

AbstractIn this chapter, we investigates the local design problem of the distributed H\(\infty \)-consensus filter for the discrete time-varying system with multiplicative noises and deception attacks over sensor networks. Two sequences of mutually uncorrelated Gaussian white noises with known statistical information are employed to model the multiplicative noises. A Bernoulli distributed random variable is utilized to formulate whether the deception attacks are successful or not. Next, the sector-like bounded condition is utilized to formulate the unknown malicious signal involved in the deception attacks. Then, the desirable H\(\infty \)-consensus performance constraint is established to tolerate a certain degree of deception attacks. In view of the stochastic vector dissipation theory, sufficient condition is established in terms of a set of matrix inequalities for each node to guarantee the prescribed H\(\infty \)-consensus performance. Furthermore, the corresponding filter gains are designed by recursively solving certain matrix inequalities.