Unscented Kalman filtering for nonlinear systems with sensor saturation and randomly occurring false data injection attacks

Abstract In this paper, an unscented Kalman filtering problem is studied for a nonlinear system with sensor saturation and randomly occurring false data injection attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring false data injection attacks. The aim of this paper is to design a modified unscented Kalman filter by minimizing an upper bound of filtering error covariance. Furthermore, a sufficient condition is provided to ensure an exponentially bounded filtering error in the mean square sense. Numerical simulations are presented to illustrate the validity of the proposed filter.