Iterated unscented Kalman filter algorithm based on filter smoothing
Aiming at the problem that the prior estimation accuracy of unscented Kalman filter(UKF)is seriously reduced in the case of strong nonlinear dynamic functions,an iterative UKF algorithm based on filter smoothing was proposed to improve the time update of UKF.By studying the partial reverse smoothing operation of the unscented Rauch-Tung-Striebel smoother,more accurate input of the filter dynamic model and more accurate state prior estimation were obtained.The proposed itera-tive UKF algorithm could be combined with the traditional iterative UKF algorithm for processing measurement updates to further improve the filter solution.The effectiveness of the proposed iterative UKF algorithm was verified by using a single variable non-stationary growth model.The results show that the performance of the proposed iterative UKF algorithm is significantly improved for nonlinear dynamic models.Moreover,combining the proposed iterative UKF algorithm with the traditional ite-rative UKF algorithm can further improve the performance of UKF.
万方数据
维普
