Robust predictive augmented unscented Kalman filter

Abstract

This paper presents a new Unscented Kalman Filtering (UKF) method by using robust model prediction. This method incorporates system driving noise in system state through augmentation of state space dimension to expand the input of system state information. The system model error is constructed by model prediction, and is then used to rectify the UKF process to obtain the estimate of the real system state. The proposed method endows the robustness to the traditional UKF, thus overcoming the limitation that the traditional UKF is sensitive to system model error. Experimental results show that the convergence rate and accuracy of the proposed filtering method is superior to the Extended Kalman Filtering and traditional UKF.