关键词: 单站目标跟踪, 非线性滤波, 容积卡尔曼滤波, 后向平滑, 野值, M估计

Abstract: M-estimated based Robust Backward-Smoothing Cubature Kalman Filter（MR-BSCKF） algorithm is proposed. The algorithm introduces the improved M-estimation idea into the Backward-Smoothing Cubature Kalman Filter（BSCKF） algorithm, introduces the Mahalanobis distance to construct the P-Huber equivalent weight function, and the robustness of the filtering algorithm is further increased by reducing outliers misjudgment. The algorithm introduces the backward-smoothing function into the traditional CKF algorithm, and the filtering accuracy is further improved by the secondary filtering combined with backward smoothing and forward filtering, that achieving filtering accuracy and robustness at the same time. The simulation results show that MR-BSCKF, compared with the traditional algorithm, can get more accurate target tracking results in the presence and absence of outliers, and the robustness is more robust.

Key words: single station target tracking, nonlinear filter, cubature Kalman filter, backward-smoothing, outliers, M-estimation