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针对广义卡尔曼滤波(extended Kalman filter,EKF)和无迹卡尔曼滤波(unscented Kalman filter,UKF)缺乏对系统异常的在线自适应调整能力、导致滤波器精度降低的问题,提出了一种将强跟踪滤波(strongtracking filter,STF)和UKF相结合的滤波算法,并进一步采用部分状态信息作为间接观测量,同时量测噪声方差阵实时调整,从而避免了对观测方程求取Jacobi矩阵的过程,使滤波器的设计得到简化。将该算法应用于航天器自主导航系统中,仿真结果表明,该算法在系统出现突变或缓变异常时,能够迅速检测出异常,在保证较高估计精度的同时,提高了系统的可靠性。
Aiming at the problem that the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) lack the ability of on-line adaptive adjustment to the system anomalies, resulting in the decrease of the filter accuracy, Filtering algorithm combined with STF and UKF, and further using some state information as indirect observation, and real-time adjustment of measurement noise variance matrix, thus avoiding the process of obtaining Jacobi matrix for observation equation, The design of the filter is simplified. The algorithm is applied to the spacecraft autonomous navigation system. The simulation results show that the algorithm can detect the abnormality rapidly when the system is abruptly or slowly changing anomaly, and improve the reliability of the system while ensuring high estimation accuracy.