Recursive Filtering for Nonlinear Systems with Measurement Saturation and Correlated Noises

This paper is concerned with the recursive filtering problem for the nonlinear systems with measurement saturation and correlated noises. The process and the measurement noises are one-step autocorrelated and two-step cross-correlated, respectively. The objective of this paper is to design a Kalman-like filter, which would minimize the upper bound of the filtering error covariance by proper design of the filter gains at each time step. The suggested scheme provides recursive computing in online applications. A numerical simulation is utilized to show effectiveness of the developed filtering algorithm.