计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (6A): 250300058-7.doi: 10.11896/jsjkx.250300058

• 图像处理&多媒体技术 • 上一篇    下一篇

时变非高斯观测噪声下雷达目标跟踪改进方法

杨翰琨1, 朱博威2, 王作帅3, 徐以东1   

  1. 1 哈尔滨工程大学烟台研究院 山东 烟台 265500
    2 北京宇航系统工程研究所 北京 100076
    3 武汉第二船舶设计研究所海洋电磁探测与控制湖北省重点实验室 武汉 430064
  • 出版日期:2026-06-16 发布日期:2026-06-12
  • 通讯作者: 王作帅(hustwzs@foxmail.com)
  • 作者简介:(yanghankun@hrbeu.edu.cn)
  • 基金资助:
    国家自然科学基金(52101383)

Improved Method for Radar Target Tracking Under Time-varying Non-Gaussian Observation Noise

YANG Hankun1, ZHU Bowei2, WANG Zuoshuai3, XU Yidong1   

  1. 1 Yantai Research Institute of Harbin Engineering University,Yantai 265500,China
    2 Beijing Institute of Astronautical Systems Engineering,Beijing 100076,China
    3 Hubei Key Laboratory of Marine Electromagnetic Detection and Control of Wuhan Second Ship Design and Research Institute,Wuhan 430064, China
  • Published:2026-06-16 Online:2026-06-12
  • About author:YANG Hankun,born in 2001,postgra-duate.His main research interest is radar signal processing.
    WANG Zuoshuai,born in 1990,Ph.D,senior engineer.His main research interest is the application and protection of electromagnetic fields in ships.
  • Supported by:
    National Natural Science Foundation of China(52101383).

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摘要： 文中提出了一种改进的最小误差熵卡尔曼滤波算法,旨在应对复杂的时变非高斯观测噪声环境中的雷达目标跟踪问题。该算法通过引入自适应噪声协方差调整策略和观测数据平滑处理技术,能够有效地动态适应海面噪声特性。通过仿真实验,在时变厚尾噪声环境下,改进算法在x方向和y方向的平均绝对误差分布降低了约67.44%和69.09%,在时变偏态噪声环境下分别降低了约71.99%和70.90%。这些结果表明,改进的MEEKF算法在处理时变非高斯观测噪声环境时具有显著的优势,提供了一种有效的解决方案。

关键词: 雷达目标跟踪, 时变非高斯噪声, 最小误差熵卡尔曼滤波, 自适应策略, 数据平滑技术

Abstract: This paper proposes an improved Minimum Error Entropy Kalman Filter algorithm aimed at addressing the radar target tracking problem in complex time-varying non-Gaussian observation noise environments.By introducing an adaptive noise covariance adjustment strategy and observation data smoothing techniques,the algorithm can dynamically and effectively adapt to sea surface noise characteristics.Through simulation experiments,it is found that in time-varying heavy-tailed noise environments,the improved algorithm reduces the average absolute error distribution in the x and y directions by approximately 67.44% and 69.09%,respectively.In time-varying skewed noise environments,the reductions are approximately 71.99% and 70.90%,respectively.These results demonstrate that the improved MEEKF algorithm has significant advantages in handling time-varying non-Gaussian observation noise environments,providing an effective solution.

Key words: Radar target tracking, Time-varying non-Gaussian noise, MEEKF, Adaptive strategy, Data smoothing techniques

中图分类号: 

  • TN953
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