【摘要】 针对强跟踪滤波器(STF)的理论局限性及不良测量导致的滤波性能下降问题,提出了一种强跟踪自适应平方根容积卡尔曼滤波(SRCKF)算法.利用新息协方差匹配原理,建立对不良测量具有鲁棒性的自适应SRCKF.基于STF的理论框架,采用自适应SRCKF代替扩展卡尔曼滤波构建强跟踪自适应SRCKF.该算法兼具STF与自适应SRCKF的优点,在系统同时存在模型不确定性及不良测量时具有良好的滤波性能.仿真验证了所建算法的有效性.更多还原

【Abstract】 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.更多还原