现代防御技术 ›› 2020, Vol. 48 ›› Issue (1): 44-51.doi: 10.3969/j.issn.1009-086x.2020.01.007

• 探测跟踪技术 • 上一篇    下一篇

基于新型智能特征集的辐射源个体识别

王上月1,2, 王悦2, 高路1,2, 刘鑫1,2, 秦鹏1,2, 曹阳1,2   

  1. 1.试验物理与计算数学国家重点实验室,北京 100076;
    2.北京航天长征飞行器研究所,北京 100076
  • 收稿日期:2018-09-05 修回日期:2019-08-27 出版日期:2020-02-20 发布日期:2021-01-20
  • 作者简介:王上月(1991-),女,黑龙江鸡西人。工程师,硕士,主要研究方向为数字信号处理。通信地址:100076 北京市9200信箱76分箱7号 E-mail:wangshy310@foxmail.com

Emitter Individual Identification Method Based on the New Intelligent Feature Set

WANG Shang-yue1,2, WANG Yue2, GAO Lu1,2, LIU Xin1,2, QIN Peng1,2, CAO Yang1,2   

  1. 1. National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics,Beijing 100076,China;
    2. Beijing Institute of Space Long March Vehicle,Beijing 100076,China
  • Received:2018-09-05 Revised:2019-08-27 Online:2020-02-20 Published:2021-01-20

摘要: 雷达发射机结构上的差异、使用电子器件的不同,决定了辐射源的唯一性。提出了基于新型智能特征集的辐射源个体识别方法,首先对接收到的信号进行时域、频域、时频域、极化域变换并提取特征,构造能够表征每个辐射源的新型智能特征集,然后为了提高运算速度同时去掉冗余信息,用主成分分析法对特征集进行主特征提取,最后再用支持向量机方法,通过选取最优RBF核函数来实现个体识别。通过仿真,验证了构建的新型智能特征集可以对辐射源进行唯一的表征,在低信噪比环境下可以实现对辐射源个体进行快速有效的识别,分别对CW,BPSK,LFM信号进行了仿真,在3 dB信噪比以上都能达到85%以上的识别率,验证所述方法的正确性和可行性。

关键词: 雷达辐射源个体识别, 新型智能特征集, 极化域, 主成分分析, 支持向量机

Abstract: The difference in the structure of radar transmitters and the use of electronic devices determine the uniqueness of the radiation source. The emitter individual identification method based on new intelligent feature set is proposed. Firstly, the received signals are transformed in time-domain, frequency-domain, time-frequency domain, polarization domain, and the features are extracted, secondly, the new intelligent feature set of each radiation source is constructed. In order to improve the computing speed and remove redundant information, master feature of the new intelligent feature set are extracted using principal component analysis, and then by choosing the optimal RBF kernel function with support vector machine (SVM), the individual identification is realized. By simulating and verifying the signals of three radiation sources, the radiation source can be characterized uniquely with the new intelligent feature set. Under low SNR, the radiation source individual can be identified quickly and effectively, by simulating the CW,BPSK,LFM signal respectively, more than 85% recognition rate can be reached in more than 3 db SNR, which verifies the validity and feasibility of the method.

Key words: radar emitter individual recognition, new intelligent feature set, polarization domain, principal component analysis, support vector machine

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