雷达辐射源个体识别综述

史亚; 张文博; 朱明哲; 王磊; 徐胜军

doi:10.11999/JEIT210161

雷达辐射源个体识别综述

doi: 10.11999/JEIT210161

1.
西安建筑科技大学信息与控制工程学院西安 710055
2.
西安电子科技大学电子工程学院西安 710071

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
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- 被引次数: 0
出版历程
- 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

Specific Radar Emitter Identification: A Comprehensive Review

1.
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2.
School of Electronic Engineering, Xidian University, Xi’an 710071, China

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

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。
- 辐射源个体识别 /
- 个体特征 /
- 机理分析 /
- 深度学习 /
- 数据集
Abstract: Specific radar emitter identification distinguishes each radar emitter based on the extracted individual features, which is crucial for electronic countermeasures. With the rapid development of deep learning, specific radar emitter identification using deep learning architecture draws great attention recently. Despite many years of research and rich achievements, there is still lack of a comprehensive review about specific radar emitter identification at present. Therefore, a systematic review is provided in this paper from four aspects: (1) the mechanism analysis of identification; (2) the handcrafted feature-based identification methods; (3) the deep learning-based identification methods; (4) and the testing datasets. Finally, the current status and the future directions are summarized, aiming at promoting the new development of specific radar emitter identification.
- Specific emitter identification /
- Individual feature /
- Mechanism analysis /
- Deep learning /
- Dataset

HTML全文

图 1 雷达辐射源个体识别系统框图

下载: 全尺寸图片幻灯片

图 2 基于深度学习的雷达辐射源识别方法示意图

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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]
其他	自激指数、频推系数、相空间特征、分形特征	2007: [3]; 2008: [4]; 2017: [75]

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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]

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