计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 226-235.doi: 10.11896/jsjkx.210600138

• 计算机网络 • 上一篇    下一篇


苏丹宁1, 曹桂涛1, 王燕楠1, 王宏2, 任赫2   

  1. 1 华东师范大学软硬件协同设计技术与应用教育部工程研究中心 上海200062
    2 中国电子科技集团第五十一研究所 上海201802
  • 收稿日期:2021-06-17 修回日期:2021-10-17 出版日期:2022-07-15 发布日期:2022-07-12
  • 通讯作者: 曹桂涛(gtcao@sei.ecnu.edu.cn)
  • 作者简介:(iranus@163.com)
  • 基金资助:

Survey of Deep Learning for Radar Emitter Identification Based on Small Sample

SU Dan-ning1, CAO Gui-tao1, WANG Yan-nan1, WANG Hong2, REN He2   

  1. 1 East China Normal University MoE Engineering Research Center of SW/HW Co-design Technology and Application,Shanghai 200062,China
    2 China Electronics Technology Group Corporation No.51 Research Institute,Shanghai 201802,China
  • Received:2021-06-17 Revised:2021-10-17 Online:2022-07-15 Published:2022-07-12
  • About author:SU Dan-ning,born in 1997,postgra-duate.Her main research interests include deep learning,radar emitter identification and so on.
    CAO Gui-tao,born in 1970,Ph.D,professor.Her main research interests include artificial intelligence,image analysis and understanding,medical big data processing.
  • Supported by:
    National Natural Science Foundation of China(61871186).

摘要: 面对复杂多变的电磁环境与新体制雷达系统,传统的雷达辐射源识别方法已无法满足需求。深度学习模型可有效提取雷达信号的脉内特征,快速准确地对低信噪比、未经分选的雷达辐射源信号进行脉内调制类型识别、型号识别与个体识别。但真实环境中雷达辐射源信号难以收集,无法满足传统的深度学习训练需要,因此小样本雷达辐射源识别是目前研究的热点与难点。文中首先对近年来将基于监督学习的多种经典深度学习方法应用于小样本雷达辐射源识别的研究进行了回顾;其次,介绍了小样本学习在雷达辐射源识别领域的研究进展;最后,基于小样本雷达辐射源识别的研究现状,总结面临的挑战,提出了对未来研究方向的展望。

关键词: 雷达辐射源识别, 脉内特征, 深度学习, 小样本

Abstract: Traditional radar emitter identification methods can no longer meet the needs of identifying new-system radar emitters in the complicate and changeable electromagnetic environment.Deep learning methods can effectively extract the intra-pulse features of the unsorting radar emitter signal,quickly and accurately identify the radar intra-pulse modulation type,model type and emitter individual under complex environments such as low signal-to-noise ratio.However,in the reality,radar emitter signal is difficult to collect and cannot satisfy the training needs of traditional deep learning models.Therefore,the small sample radar emitter identification is one of hotspot and difficult questions of current research.Firstly,this paper reviews the research progress and application of various deep learning methods based on supervised learning for radar emitter recognition with small samples in recent years.Secondly,the research progress of radar emitter identification by small sample learning is introduced.Last,according to the current radar emitter identification research,the challenges and outlook for future research are put forward.

Key words: Deep learning, Intra-pulse feature, Radar emitter identification, Small sample


  • TP181
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