上海交通大学学报

上海交通大学学报 ›› 2019, Vol. 53 ›› Issue (9): 1091-1099.doi: 10.16183/j.cnki.jsjtu.2019.09.011

• 学报（中文） • 上一篇    下一篇

基于支持向量机的雷达电子支援措施系统点迹-航迹关联算法

王江卓1,徐文聪1,李建勋1,贺丰收2,曹兰英2,缪礼锋2   

  1. 1. 上海交通大学 自动化系， 上海 200240； 2. 中航工业雷华电子技术研究所， 江苏 无锡 214063
  • 发布日期:2019-10-11
  • 通讯作者: 李建勋，男，教授，博士生导师，电话(Tel.): 021-34204305；E-mail：lijx@sjtu.edu.cn.
  • 作者简介:王江卓（1993-），男，河北省石家庄市人，硕士生，主要研究方向为模式识别与智能系统.
  • 基金资助:
    装备预研领域基金项目(61404130103)，2015 中航工业产学研专项，国家自然科学基金 (61673265)，国家重点基础研究发展计划(6133190302)资助项目

Dot-Track Association Algorithm for Radar Electronic Support Measurement Systems Based on Support Vector Machine

WANG Jiangzhuo 1,XU Wencong 1,LI Jianxun 1,HE Fengshou 2,CAO Lanying 2,MIAO Lifeng 2   

  1. 1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; 2. AVIC Leihua Electronic Technology Research Institute, Wuxi 214063, Jiangsu, China
  • Published:2019-10-11

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摘要： 概率数据关联是多源信息融合算法中的关键问题，本文主要对基于雷达和电子支援措施(ESM)双传感器融合的数据关联问题展开研究.由于雷达和ESM传感器方位角数据分布近似相同，可以通过对ESM数据的分析得到判别函数，使用相应的判决规则对点迹和航迹进行关联，这本质上可以看作是一个模式识别问题.本文考虑到支持向量机(SVM)模型在模式分类中的良好表现，建立了基于SVM的雷达ESM系统的点迹-航迹关联模型，使用ESM传感器航迹数据训练SVM模型，对雷达点迹数据进行分类，获得关联结果.最终模拟结果表明：与经典的多假设跟踪算法相比，所提出的算法可有效提高关联准确率.

关键词: 雷达； 电子支援措施(ESM)； 航迹关联； 支持向量机(SVM)

Abstract: Probabilistic data association is an important issue in multi-source information fusion algorithms. The data association problem based on radar and electronic support measurement (ESM) sensors is mainly discussed in this paper. As the azimuthal data of radar and ESM sensors have approximately the same distribution, the discriminant function can be obtained through the analysis of ESM data, and the corresponding decision rules can be used to associate the dots and tracks. The association issue can be essentially regarded as a pattern recognition problem. In this paper, considering the good performance of support vector machine(SVM) in pattern classification, we establish a dotting and tracking association model for radar ESM systems based on SVM algorithm. We train the SVM model with ESM data, and classify the radar data to acquire association result. Finally, the simulation results show that the association accuracy can be effectively improved compared with the classical multiple hypothesis tracking algorithm.

Key words: radar; electronic support measurement (ESM); tracking association; support vector machine (SVM)

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