存在非高斯重尾分布噪声的纯方位目标跟踪算法

刘灿; 王辉; 林德福; 崔晓曦; 徐晗晖

doi:10.12382/bgxb.2022.0058

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存在非高斯重尾分布噪声的纯方位目标跟踪算法

更新时间：2025-08-18

- 存在非高斯重尾分布噪声的纯方位目标跟踪算法
- Bearings-Only Target Tracking Algorithm with Non-Gaussian Heavy-Tailed Distributed Noise
- 兵工学报 2023年44卷第5期页码：1469-1481
- 作者机构：
  
  1. 北京理工大学宇航学院, 北京 100081
  2. 中国兵器工业导航与控制技术研究所, 北京 100089
  3. 北京理工大学设计与艺术学院, 北京 100081
- 作者简介：
  
  *邮箱: E-mail:wanghuibit@bit.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61827901)
- DOI：10.12382/bgxb.2022.0058
  中图分类号：
- 收稿：2022-01-23，
  
  网络出版：2023-07-19，
  
  纸质出版：2023-05-31
- 稿件说明：
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刘灿, 王辉, 林德福, 等. 存在非高斯重尾分布噪声的纯方位目标跟踪算法[J]. 兵工学报, 2023,44(5):1469-1481.

Can LIU, Hui WANG, Defu LIN, et al. Bearings-Only Target Tracking Algorithm with Non-Gaussian Heavy-Tailed Distributed Noise[J]. Acta Armamentarii, 2023, 44(5): 1469-1481.
刘灿, 王辉, 林德福, 等. 存在非高斯重尾分布噪声的纯方位目标跟踪算法[J]. 兵工学报, 2023,44(5):1469-1481. DOI： 10.12382/bgxb.2022.0058.

Can LIU, Hui WANG, Defu LIN, et al. Bearings-Only Target Tracking Algorithm with Non-Gaussian Heavy-Tailed Distributed Noise[J]. Acta Armamentarii, 2023, 44(5): 1469-1481. DOI： 10.12382/bgxb.2022.0058.

摘要

纯方位目标跟踪是目标跟踪研究中的热点问题

针对目标跟踪方程中的非高斯重尾分布噪声问题

提出了一种针对非高斯重尾分布噪声的卡尔曼滤波算法。该方法通过建立基于存在异常值的高斯分布的层次高斯模型来近似未知的非高斯重尾分布系统过程噪声和测量噪声

并使用变分贝叶斯推断来学习混合概率

解决混合概率不确定带来的滤波性能下降的问题

从而提高滤波的鲁棒性。同时针对纯方位目标跟踪模型的非线性

结合修正增益卡尔曼滤波来降低量测方程非线性的影响。数值仿真结果表明

相对于EKF、UKF和变分贝叶斯卡尔曼滤波PEKF-VB、VBEKF

新算法VBMGEKF估计精度分别提高了69.31%、58.08%、127.84%和9.36%

具备更好的鲁棒性与精度。

Abstract

The bearings-only target tracking is a classic problem in the reserach on target tracking. Focusing on the problem of non-Gaussian heavy-tailed distributed noise in the model of target tracking

this paper proposes a new Kalman filter algorithm. Firstly

the hierarchical Gaussian model is established to approximate the unknown process noise and measurement noise of the non-Gaussian heavy-tailed distributed system. Next

the variational Bayesian inference is used to learn Mixture Probability to solve the problem of the filter’s performance degradation caused by the uncertainty of Mixture Probability

so as to improve the robustness of the filter. At the same time

for the nonlinearity of the bearings-only target tracking model

Modified Gain Kalman filter is used to reduce the influence of nonlinearity on the observation equation. The numerical simulations have verified that the proposed filter has better estimation accuracy and robustness than EKF

UKF and the variational Bayesian Kalman filters PEKF-Vb and VBEKF. The estimation accuracy of the proposed algorithm VBMGEKF is improved by 69.31%

58.08%

127.84% and 9.36%.

关键词

Keywords

references

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