Abstract:

An iterated cubature Kalman filter (ICKF) is proposed, which combines the GaussNewton iterate method with the cubature Kalman filter (CKF). In the ICKF algorithm, cubature rule based numerical integration method is directly used to calculate the mean and covariance of the nonlinear random function, and the latest measurement, improved innovation covariance and crosscovariance are iteratively used in the measurement update, so the higher accuracy of state estimate is achieved. The ICKF algorithm is applied to state estimation for reentry ballistic target with unknown ballistic coefficient. The simulation results indicate that the implementation of the proposed method is easy and simple. Moreover, the higher accuracy of state estimation is obtained compared with UKF and CKF.