黄知涛,,
王翔,,
王丰华,
李保国
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：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)
SUN Liting,HUANG Zhitao,,
WANG Xiang,,
WANG Fenghua,
LI Baoguo
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。
关键词: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 (SEI) is a technique of extracting the radio frequency fingerprints of a received electromagnetic signal by only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of SEI have been continuously improved, and the research on Radio Frequency Fingerprinting (RFF) feature extraction methods has made great progress. Based on domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of SEI. 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 RFF features and the correlation between them. It divides the existing radio frequency features into two main categories, namely, direct measurement features and dimensionality reduction transform features, which have three levels. Finally, several potential research directions of fingerprint feature extraction are analyzed and explored, aiming to benefit the research and application of SEI.
Key words:Specific emitter identification/
Radio frequency fingerprint extraction/
Pattern recognition/
Signal processing/
Feature framework
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