辐射源指纹特征提取方法述评

孙丽婷; 黄知涛; 王翔; 王丰华; 李保国

doi:10.12000/JR19115

辐射源指纹特征提取方法述评

DOI: 10.12000/JR19115

国防科技大学电子科学学院长沙 410073

基金项目: 湖南省创新群体研究项目(2019JJ10004)

详细信息

作者简介:
孙丽婷(1994–) 女，山东人，博士研究生，主要研究方向为通信辐射源个体识别。E-mail: slt2009@yeah.net

黄知涛(1976–)，男，湖北人，教授，博士生导师，主要研究方向为航天电子侦察、雷达/通信信号处理、综合电子战系统与技术等。E-mail: huangzhitao@nudt.edu.cn

王　翔(1985–)，男，福建人，讲师，主要研究方向为航天电子侦察、信号处理、模式识别等。E-mail: christopherwx@163.com

王丰华(1981–)，男，山东人，讲师，主要研究方向为信号处理、模式识别等。E-mail: wfh.abc@163.com

李保国(1977–)，男，湖北人，副教授，主要研究方向为通信信号处理等。E-mail: laglbg322@163.com

通讯作者:
黄知涛 huangzhitao@nudt.edu.cn

王翔 christopherwx@163.com

责任主编：黄高明 Corresponding Editor: HUANG Gaoming
中图分类号: TN97
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出版历程
- 收稿日期: 2019-12-19
- 修回日期: 2020-04-10
- 网络出版日期: 2020-05-07

Overview of Radio Frequency Fingerprint Extraction in Specific Emitter Identification（in English）

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China (2019JJ10004)

More Information

Corresponding author: HUANG Zhitao, huangzhitao@nudt.edu.cn; WANG Xiang, christopherwx@163.com

摘要

摘要: 辐射源个体识别是一种仅通过信号的外部特征测量手段，提取辐射源指纹特征，从而识别发射给定信号的特定辐射源个体的技术。近年来，辐射源个体识别技术相关理论与实践应用不断完善，指纹特征提取方法的研究取得了较大的进展。该文在分析国内外大量学术研究成果的基础上，从指纹特征的内在逻辑出发提出了一种新的特征框架。该框架根据不同特征对辐射源指纹的描述特性以及相互之间的关联，将指纹特征划分为直接测量特征和降维变换特征两大类共3个层次，并系统性地梳理了辐射源指纹特征提取方法的研究现状。最后，该文对辐射源指纹特征提取的几个潜在研究方向进行了分析和展望, 希望对辐射源个体识别的研究和应用有所裨益。
- 辐射源个体识别 /
- 指纹特征提取 /
- 模式识别 /
- 信号处理 /
- 特征框架
Abstract: Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.
- Specific emitter identification /
- Radio frequency fingerprint extraction /
- Pattern recognition /
- Signal processing /
- Feature framework

HTML全文

图 1 辐射源个体识别经典系统结构图^[1]

Figure 1. Structural diagram of typical system for specific emitter identification^[1]

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图 2 雷达脉冲包络

Figure 2. Radar pulse envelope

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图 3 4架民航飞机2次应答信号瞬时相位

Figure 3. Instantaneous phase characteristics of the secondary response signals of four civil aviation aircrafts

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图 4 两部FM电台信号双谱特征图

Figure 4. Bispectrum characteristic images of two FM radio signal

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图 5 IEEE802.11b协议的导头格式

Figure 5. Preamble format of standard IEEE802.11b

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图 6 经验模态分解结果示意图

Figure 6. Schematic of empirical mode decomposition results

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图 7 主曲线提取数据骨架

Figure 7. Data skeleton extracted by principal curve

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图 8 不同路径积分双谱

Figure 8. Integral paths of bispectrum

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图 1 Structural diagram of typical system for specific emitter identification^[1]

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图 2 Radar pulse envelope

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图 3 Instantaneous phase characteristics of the secondary response signals of four civil aviation aircrafts

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图 4 Bispectrum characteristic images of two FM radio signal

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图 5 Preamble format of standard IEEE802.11b

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图 6 Schematic of empirical mode decomposition results

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图 7 Data skeleton extracted by principal curve

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图 8 Integral paths of bispectrum

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表 1 基于指纹分析内在逻辑的指纹特征分类框架下的直接测量特征

Table 1. Direct measurement features under the feature framework based on the inherent logic of fingerprint feature extraction

特征方法		相关文献
基本参数信息	常规参数	文献[1,11,12]
	包络特性	文献[13—18]
	瞬时特性	文献[19—23]
	调制参数	文献[24—26]
	频谱分布	文献[3—27]
基本变换信息	时频谱	文献[28—32]
	高阶谱	文献[33—37]
	循环谱	文献[38,39]
	Hilbert谱	文献[40—43]
信号特殊结构	信号导头	文献[23,44,45]
信号特殊结构	雷达信号分析	文献[46–51]
分解重构信息	经验模态分解	文献[42,43,52—55]
	固有时间尺度分解	文献[56—63]
	变分模态分解	文献[9,43,64,65]
	相空间重构	文献[2,66—69]
	信号分割重构	文献[70—71]
发射机硬件特性	等效电路	文献[72]
	非线性电路	文献[73]
	频率源	文献[74]

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表 2 基于指纹分析内在逻辑的指纹特征分类框架下的降维变换特征

Table 2. Dimensionality reduction feature under feature framework based on the inherent logic of fingerprint feature extraction

特征方法	相关文献
波形骨架	文献[75—78]
分形维数、复杂度	文献[42,79,80]
特定路径积分切片	文献[38,39,81]
熵值计算	文献[59,65,82—84]
奇异值分解等	文献[36,38,85]
传统降维特征	文献[3,86—88]

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表 1 Direct measurement features under the feature framework based on the inherent logic of fingerprint feature extraction

Feature method		Reference
Basic parameter information	General parameter	[1,11,12]
	Envelope feature	[13–18]
	Instantaneous feature	[19–23]
	Modulation parameter	[24–26]
	Spectrum feature	[3–27]
Basic transformation information	Time-frequency analysis	[28–32]
	High-order spectrum	[33–37]
	Cyclic spectrum	[38,39]
	Hilbert spectrum	[40–43]
Signal special structure	Signal preamble	[23,44,45]
Signal special structure	Radar signal analysis	[46–51]
Decomposition and reconstruction information	EMD	[42,43,52–55]
	ITD	[56–63]
	VMD	[9,43,64,65]
	Phase space analysis	[2,66–69]
	Segmentation reconstruction	[70,71]
Transmitter hardware characteristics	Equivalent circuit model	[72]
	Nonlinear circuit model	[73]
	Frequency source circuit	[74]

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表 2 Dimensionality reduction feature under feature framework based on the inherent logic of fingerprint feature extraction

Feature Method	Reference
Waveform skeleton	[75–78]
Fractal and complexity	[42,79,80]
Specific path integration/slicing	[38,39,81]
Entropy	[59,65,82–84]
SVD	[36,38,85]
Traditional dimensionality reduction	[3,86–88]

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