Kalman-type recursive filtering for stochastic nonlinear time-delay systems with randomly occurring deception attacks
Shuai Liu, Guoliang Wei, Derui Ding, Jingyang Mao · 2017
In this paper, the Kalman-type recursive filtering problem is investigated for a class of discrete-time stochastic non-linear systems subject to time-delays and randomly occurring deception attacks. The deception attack under consideration is assumed to be bounded and occur in a random way, which is characterized by using the Bernoulli distribution white sequences. The purpose of the problem addressed in this paper is to design the Kalman-type recursive filter such that for all admissible randomly occurring cyber attacks, time-delays, Gaussian noises, deterministic nonlinearities and stochastic nonlinearities, an upper bound of the filtering error covariance matrix can be obtained in terms of two Riccati-like difference equations. Subsequently, by minimizing such an upper bound, the optimized filter gain can be derived. Finally, a simulation example is proposed to illustrate the effectiveness of the proposed Kalman-type recursive filter design scheme.