系统工程与电子技术 ›› 2019, Vol. 41 ›› Issue (2): 298-303.doi: 10.3969/j.issn.1001-506X.2019.02.11

• 传感器与信号处理 • 上一篇    下一篇

干扰规则库未知条件下的干扰决策

邢强1, 朱卫纲1, 贾鑫1, 郑光勇2   

  1. 1. 航天工程大学电子与光学工程系, 北京 101416;
    2. 电子信息系统复杂电磁环境效应国家重点实验室, 河南 洛阳 471003
  • 出版日期:2019-01-25 发布日期:2019-01-25

Jamming decision under condition of unknown jamming rule base

XING Qiang1, ZHU Weigang1, JIA Xin1, ZHENG Guangyong2   

  1. 1. Department of Electronic and Optical Engineering, Space Engineering University, Beijing 101416, China;
    2. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China
  • Online:2019-01-25 Published:2019-01-25

摘要: 针对模板匹配(template matching, TM)应用于未知干扰规则库时干扰决策正确率低问题,提出基于迁移成分分析-支持向量机(transfer component analysissupport vector machine, TCASVM)的干扰决策方法。对空-空场景机载多功能火控雷达,提取雷达信号特征,构建雷达干扰规则库及未知威胁数据集,通过迁移成分分析把两个样本集的特征映射到同一低维隐藏空间,提取样本隐藏空间特征,经过支持向量机训练,实现对未知威胁数据集的干扰决策。实验结果表明:所提方法有效提高了干扰决策正确率,TCA-SVM出色的学习及泛化能力,较好地解决了干扰规则库未知条件下干扰决策问题。

Abstract: To solve the problem of low jamming decision accuracy of template matching (TM) method under condition of unknown jamming rule base, a method based on transfer component analysissupport vector machine (TCASVM) is proposed. For airborne multifunctional fire control radar on airtoair scene, the signal features are extracted and the jamming rule base and unknown threat dataset are constructed. The features of the two sample sets are mapped to the same low dimension hidden space via transfer component analysis. The hidden space sample features are extracted and trained by support vector machine to realize jamming decision on the unknown threat dataset. The experiment results show that the proposed method can effectively improve the jamming decision accuracy rate. The jamming decision problem under the condition of unknown jamming rule base is solved because of excellent learning and generalization ability of TCASVM.