Strong Tracking Adaptive Square-Root Cubature Kalman Filter Algorithm
XU Shu-shen · Dianzi xuebao · 2014
To overcome problems that malfunctions in the measurement system lead to degradation of performance of strong tracking filter(STF)and inherent disadvantages of STF,an adaptive square-root cubature Kalman filter(SRCKF)algorithm is proposed.With innovation covariance matching techniques an adaptive SRCKF is built,which is insensitive to measurement malfunctions.Strong tracking adaptive SRCKF views STF as the basic theory framework and makes adaptive SRCKF to replace extended Kalman filter(EKF),so it has the advantages of STF and adaptive SRCKF.In case of model uncertainty of system and measurement malfunctions,the proposed algorithm has strong robustness and high accuracy.Simulation results show the effectiveness of the presented algorithm.