An adaptive nonlinear filter of discrete-time system with uncertain covariance using unscented Kalman filter

A novel adaptive unscented Kalman nonlinear filter (AUKF) is presented in this paper. In many system, the noise covariance is unknown exact, but the approximate can been obtained by many methods. The approximate is used to initialize the unscented Kalman filter (UKF). Each step these noise covariance are adjusted based on the prior noise covariance and state information. To reduce obsolete measure value and covariance, a limited memory method is used. On the performance, UKF is better than EKF. The AUKF are better than these adaptive Kalman filters which based on extended Kalman filter (EKF). A target tracking is used to demonstrate this.