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

LIN Jie; QI Wang-dong; ZHAO Yue-xin; LIU Peng

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

更新时间：2026-09-04

- Robust bias compensation Kalman filter algorithm for three-dimensional AoA-ToA target tracking
- Vol. 38, Issue 6, Pages: 53-59(2021)
- 作者机构：
  
  1. 陆军工程大学指挥控制工程学院,江苏,南京,210007
  2. 东南大学信息科学与工程学院,江苏,南京,210096
  3. 网络通信与安全紫金山实验室,江苏,南京,211111
- 作者简介：
- 基金信息：
- DOI：
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- Published：2021
- 稿件说明：
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林杰, 齐望东, 赵跃新, 刘鹏. 三维AoA-ToA目标跟踪的鲁棒偏差补偿卡尔曼滤波算法[J]. 微电子学与计算机, 2021, 38(6): 53-59, 65.

LIN Jie, QI Wang-dong, ZHAO Yue-xin, et al. Robust bias compensation Kalman filter algorithm for three-dimensional AoA-ToA target tracking[J]. 2021, 38(6): 53-59.
林杰, 齐望东, 赵跃新, 刘鹏. 三维AoA-ToA目标跟踪的鲁棒偏差补偿卡尔曼滤波算法[J]. 微电子学与计算机, 2021, 38(6): 53-59, 65. DOI：

LIN Jie, QI Wang-dong, ZHAO Yue-xin, et al. Robust bias compensation Kalman filter algorithm for three-dimensional AoA-ToA target tracking[J]. 2021, 38(6): 53-59. DOI：

摘要

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.

Abstract

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.

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references

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