引用本文:白向龙,潘泉,马恩淳,郝宇航,云涛.基于消息传递的机载雷达组网航迹融合[J].控制理论与应用,2024,41(7):1235~1245.[点击复制]
BAI Xiang-long,PAN Quan,MA En-chun,HAO Yu-hang,YUN Tao.Track fusion for airborne radar network using message passing[J].Control Theory and Technology,2024,41(7):1235~1245.[点击复制]
基于消息传递的机载雷达组网航迹融合
Track fusion for airborne radar network using message passing
摘要点击 77  全文点击 37  投稿时间：2023-09-09  修订日期：2024-06-19
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DOI编号  DOI:10.7641/CTA.2024.30611
  2024,41(7):1235-1245
中文关键词  航迹融合  消息传递  概率图模型  平均场近似  置信传播
英文关键词  track fusion  message passing  probabilistic graphic model  mean field approximation  belief propagation
基金项目  国家自然科学基金项目(62233014)资助.
作者单位E-mail
白向龙 西北工业大学 baixianglong@mail.nwpu.edu.cn 
潘泉* 西北工业大学 quanpan@nwpu.edu.cn 
马恩淳 西北工业大学  
郝宇航 西北工业大学  
云涛 西北工业大学  
中文摘要
      机载雷达组网航迹融合需要解决目标跟踪、数据关联与航迹管理3个子问题, 然而这3个子问题相互耦合,采用开环序贯估计算法会导致性能下降. 本文提出了一种基于消息传递的机载雷达组网航迹融合方法, 该方法在联合优化框架下解决目标跟踪、数据关联与航迹管理3个子问题. 首先, 建立机载雷达组网航迹融合的联合概率密度函数, 并将其转换为因子图. 其次, 将因子图分解为置信传播区域与平均场近似区域. 目标运动状态的统计模型服从共轭指数模型, 因此采用平均场近似以获得简单的消息传递更新公式. 数据关联包含一对一约束, 因此采用置信传播. 目标存在状态同样采用置信传播, 以获得更好的近似结果. 最后, 可以通过闭环迭代框架近似估计后验分布, 从而有效处理目标跟踪、数据关联与航迹管理之间的耦合问题. 仿真结果表明, 所提算法的性能优于多假设跟踪算法和联合概率密度关联算法.
英文摘要
      Airborne radar network track fusion requires three sub-problems: target tracking, data association and track management, which are coupled with each other and therefore require a joint solution. In this paper, we propose a track fusion method for airborne radar network using massage passing, which jointly solves the three sub-problems of target tracking, data association and track management. Firstly, the joint probability density function of airborne radar network track fusion is established, and its factorised form is converted into a factor graph. The statistical model of the target kinematic state is a conjugate exponential, and the mean-field approximation is used to obtain a simple message passing. The data association contains one-to-one constraints, and belief propagation is used. The target visibility state is also approximated by belief propagation to obtain better approximation results. Finally, the posterior probability densities can be approximated by a closed-loop iterative framework to effectively deal with the coupling problem between target tracking, data association and track management. Simulation results show that the proposed algorithm outperforms the multiple hypothesis tracking algorithm and the joint probability density association algorithm.