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雷达辐射源个体识别综述

史亚 张文博 朱明哲 王磊 徐胜军

史亚, 张文博, 朱明哲, 王磊, 徐胜军. 雷达辐射源个体识别综述[J]. 电子与信息学报, 2022, 44(6): 2216-2229. doi: 10.11999/JEIT210161
引用本文: 史亚, 张文博, 朱明哲, 王磊, 徐胜军. 雷达辐射源个体识别综述[J]. 电子与信息学报, 2022, 44(6): 2216-2229. doi: 10.11999/JEIT210161
SHI Ya, ZHANG Wenbo, ZHU Mingzhe, WANG Lei, XU Shengjun. Specific Radar Emitter Identification: A Comprehensive Review[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2216-2229. doi: 10.11999/JEIT210161
Citation: SHI Ya, ZHANG Wenbo, ZHU Mingzhe, WANG Lei, XU Shengjun. Specific Radar Emitter Identification: A Comprehensive Review[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2216-2229. doi: 10.11999/JEIT210161

雷达辐射源个体识别综述

doi: 10.11999/JEIT210161
基金项目: 国家自然科学基金(61803293, 61501357, 61301286, 61203137),陕西省自然科学基础研究计划(2019JQ760)
详细信息
    作者简介:

    史亚:女,1985年生,博士,讲师,研究方向为机器学习、雷达辐射源识别

    张文博:男,1985年生,博士,副教授,研究方向为人工智能、辐射源识别

    朱明哲:男,1982年生,博士,副教授,研究方向为非平稳信号处理

    王磊:男,1979年生,博士,副教授,研究方向为信号处理、机器学习、雷达对抗等

    徐胜军:男,1976年生,博士,副教授,研究方向为视觉感知、人工智能与智动化系统等

    通讯作者:

    史亚 shiyaworld@163.com

  • 中图分类号: TN974

Specific Radar Emitter Identification: A Comprehensive Review

Funds: The National Natural Science Foundation of China (61803293, 61501357, 61301286, 61203137), The Natural Science Basic Research Plan in Shaanxi Province of China (2019JQ760)
  • 摘要: 雷达辐射源个体识别通过提取个体特征来辨识雷达个体,是电子对抗领域的热点研究方向。近年来随着深度学习的飞速发展及其在各领域的成功应用,基于深度学习的雷达辐射源个体识别成为焦点。虽然研究多年,成果丰富,但目前尚缺少关于该方向全面、细致的综述。基于此,该文从雷达辐射源个体特征机理分析、基于手工特征的识别方法、基于深度学习的识别方法以及数据集构建4个方面着手,对雷达辐射源个体识别开展系统的综述工作,并对当前现状和未来方向进行总结与展望,旨在推动雷达辐射源个体识别理论和方法研究的新发展。
  • 图  1  雷达辐射源个体识别系统框图

    图  2  基于深度学习的雷达辐射源识别方法示意图

    表  1  雷达辐射源信号手工特征提取方法汇总

    特征域手工特征代表文献(年份:参考文献编号)
    时域UAMOP曲线、瞬时幅度波形、包络2005: [39]; 2011: [36]; 2013: [21]; 2015: [42]; 2016: [41]
    UFMOP曲线、瞬时频率波形、UPMOP曲线2004: [30]; 2005: [39]; 2012: [38]; 2013: [21]; 2015: [42]; 2016: [41]; 2020: [40]
    频率偏差曲线、频率漂移曲线1993: [8]; 2018: [43]
    频域载频均值与方差/偏移值、频谱2005: [14]; 2011: [6]; 2012: [20]; 2020: [48]
    功率谱密度、频域分布密度、频谱非对称特征2011: [6]; 2016: [31]; 2017: [47]
    双谱能量幅度与频率、双谱幅度谱及其截面2005: [14]; 2019: [49]
    双谱对角切片/对角积分/围线积分/拉东变换2008: [34]; 2012: [20]; 2015: [51]; 2019:[50]; 2020: [52]
    双谱2维降维、双谱第1象限、循环谱(双谱)2011: [36]; 2018: [53]; 2019: [50]; 2020: [54,55]
    时频域短时傅里叶变换、小波变换、小波包变换2003: [29]; 2005: [14,61]; 2010: [33]; 2017: [56]; 2018: [13,57,58,60]; 2019: [59,62]
    同步压缩变换、双线性时频分布(Cohen类)1994: [27]; 2003: [29]; 2019:[50]; 2020: [63,64]
    模糊函数2008: [65]; 2009: [35]; 2011: [6]; 2017: [67]; 2018: [66]; 2020: [68]
    稀疏时频分析、经验模态分解2009: [69]; 2012: [70]; 2019: [71]
    固有时间尺度分解、变分模态分解2019: [72]; 2020: [32,73]
    其他包络瞬态特征及其相关特征2004: [30]; 2005:[14]; 2010: [46]; 2018: [45]; 2020: [44,74]
    自激指数、频推系数、相空间特征、分形特征2007: [3]; 2008: [4]; 2017: [75]
    下载: 导出CSV

    表  2  基于深度学习的雷达辐射源个体识别方法汇总

    深度模型代表文献(年份:参考文献编号)
    深度RBM网络2016: [22]; 2018: [57]; 2019: [44]; 2020: [52,68]
    深度AE网络2017: [56]; 2018: [60]; 2020: [63]
    1维卷积网络2017: [67]; 2018: [45,86]; 2020: [48,87,88]
    2维卷积网络2018: [15,58,90]; 2020: [89,91]
    混合深度网络2019: [59]; 2020: [40]
    对抗网络2020: [54,55,93]
    下载: 导出CSV
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  • 收稿日期:  2021-02-25
  • 修回日期:  2022-04-21
  • 网络出版日期:  2022-04-26
  • 刊出日期:  2022-06-21

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