Nonlinear distributed filtering for time-varying saturated stochastic systems with cyber-attack via FlexRay regulation mechanism

Abstract

In this paper, the distributed recursive filtering (DRF) issue is addressed for time-varying saturated stochastic systems, where the communications between sensors and filters undergo the FlexRay regulation protocol (FRRP) and false data injection attack. For the sake of mitigating undesirable data congestion, the FRRP is implemented in the communication channel to regulate data transmission. The false data injection attack might occur in the transmission process owing to open yet vulnerable networked environments. The focus of this paper is to develop a novel resource-saving DRF scheme and performance evaluation criterion under the framework of variance constraint. Specifically, an upper bound on the filtering error covariance (UBFEC) is firstly derived and then such a UBFEC is minimized via selecting a proper filter gain. Moreover, the boundedness of UBFEC is examined according to the matrix theory and the theoretical proof is presented. Finally, a comparative simulation experiment with practical application in mass–spring system is exploited to verify the validity of the newly designed DRF strategy.