三维AoA-ToA目标跟踪的鲁棒偏差补偿卡尔曼滤波算法

Robust bias compensation Kalman filter algorithm for three-dimensional AoA-ToA target tracking

在以到达角(AoA)和到达时间(ToA)为观测量的三维目标跟踪中,异常值导致非线性滤波性能明显下降甚至发散.针对该问题,提出了一种基于M估计的鲁棒偏差补偿卡尔曼滤波算法(MR-BCKF).该算法首先利用AoA和ToA的等价几何关系对非线性观测方程进行伪线性化,接着依据M估计准则推导鲁棒伪线性卡尔曼滤波,然后采用偏差补偿策略提高跟踪精度.MR-BCKF利用马氏距离判别异常值,不依赖于噪声统计特性,并且通过构建改进的三段式权重函数增强鲁棒性.仿真结果表明,MR-BCKF相较于其他鲁棒滤波算法不仅能提高孤立型异常值的抑制效果,而且在连续型异常值情况下也取得更高的跟踪精度.

In the three-dimensional target tracking with angle of arrival(AoA)and time of arrival(ToA)measurements,the outliers lead to significant performance degradation or even divergence of nonlinear filter.Aiming at this problem,an M-estimation-based robust bias compensation Kalman filter(MR-BCKF)algorithm is proposed in this paper.The algorithm pseudo-linearizes the nonlinear measurement equations through the equivalent geometric relationship between AoA and ToA,and then derives the robust pseudo-linear Kalman filter by exploiting the M estimation criterion,followed by the bias compensation to improve the tracking accuracy.Moreover,the MR-BCKF uses Mahalanobis distance to distinguish outliers,which does not depend on the noise statistics,and enhances the robustness by constructing an improved three-segment weight function.Simulation results shows that compared with other robust Kalman filter,the MR-BCKF can not only improve the suppression effect of isolated outliers,but also achieve higher tracking accuracy in the case of continuous outliers.