Cubature Kalman Filter Based on Strong Tracking

Abstract

To improve the ability of dealing with inaccurate of model and statistic characteristics of noise, as well as the abrupt change of state of cubature Kalman filter (CKF), a new nonlinear filter, called Cubature Kalman Filter based on strong tracking (CKF-ST), is proposed in this paper. Inspired by the idea of strong tracking, a time-variant factor is introduced into the recursive process of cubature Kalman filter such that the filter gain can be updated along with the measured values, thus endowing CKF-ST powerful ability to deal with abrupt changes of state. Meanwhile, such merits of CKF as high accuracy and being easy to implement can be entirely preserved in CKF-ST. Simulation results on one classical examples demonstrate that CKF-ST is overall superior to CKF and other filters involved, especially when target motion changes suddenly.