UKF based fault detection and state estimation for nonlinear systems with correlated noise

State estimation and fault diagnosis are essential topics for dynamic systems. Unscented Kalman filter(UKF) has been widely applied in nonlinear systems. The classical UKF algorithm is built on the premise that process noise and measurement noise is independent. In practical problems, this assumption is not always satisfied. In addition, due to the limitation of communication and sensor fault, etc., data missing or unreliable measurements will happen inevitably. Therefore, it is very important to study the state estimation of nonlinear systems with unreliable measurements and correlated noise. In this paper, an UKF based state estimation algorithm with unreliable observations under correlated noise is presented. A numerical example is given to show the feasibility and effectiveness of the presented algorithm.