基于Raw I/Q和深度学习的射频指纹识别方法综述

陈翔 汪连栋 许雄 申绪涧 冯蕴天

陈翔, 汪连栋, 许雄, 等. 基于Raw I/Q和深度学习的射频指纹识别方法综述[J]. 雷达学报, 2023, 12(1): 214–234. doi: 10.12000/JR22140
引用本文: 陈翔, 汪连栋, 许雄, 等. 基于Raw I/Q和深度学习的射频指纹识别方法综述[J]. 雷达学报, 2023, 12(1): 214–234. doi: 10.12000/JR22140
CHEN Xiang, WANG Liandong, XU Xiong, et al. A review of radio frequency fingerprinting methods based on Raw I/Q and deep learning[J]. Journal of Radars, 2023, 12(1): 214–234. doi: 10.12000/JR22140
Citation: CHEN Xiang, WANG Liandong, XU Xiong, et al. A review of radio frequency fingerprinting methods based on Raw I/Q and deep learning[J]. Journal of Radars, 2023, 12(1): 214–234. doi: 10.12000/JR22140

基于Raw I/Q和深度学习的射频指纹识别方法综述

doi: 10.12000/JR22140
基金项目: 国家自然科学基金(61771154)
详细信息
    作者简介:

    陈 翔,博士,助理研究员,主要研究方向为复杂电磁环境特性与模拟、射频指纹识别

    汪连栋,博士,研究员,主要研究方向为复杂电磁环境效应、雷达对抗仿真试验与评估

    许 雄,博士,副研究员,主要研究方向为体系对抗试验、电磁态势认知、电磁环境模拟

    申绪涧,博士,研究员,主要研究方向为复杂电磁环境效应、雷达对抗仿真试验与评估

    冯蕴天,博士,工程师,主要研究方向为电磁大数据和智能博弈推演

    通讯作者:

    陈翔 cemee_xchen@163.com

    许雄 xuxiong2008@foxmail.com

  • 责任主编:黄知涛 Corresponding Editor: HUANG Zhitao
  • 中图分类号: TN974

A Review of Radio Frequency Fingerprinting Methods Based on Raw I/Q and Deep Learning

Funds: The National Natural Science Foundation of China (61771154)
More Information
  • 摘要: 硬件差异会形成辐射源的独有指纹,并附加在无线电信号上,利用辐射源的这一独特属性可进行射频指纹识别。在非合作条件下,由于信道环境未知、信号调制方案等先验知识匮乏,基于特征工程的射频指纹识别方法面临巨大挑战,而基于深度学习的射频指纹识别方法,尤其是能够直接处理Raw I/Q的方法表现出了很大潜力,但是该方向的研究成果较为零散,妨碍了研究者对关键问题的把握。该文首先从先验知识的利用上,对基于深度学习的射频指纹识别方法进行了分类对比,将问题聚焦到基于Raw I/Q和深度学习的射频指纹识别方法。然后,该文重点对使用Raw I/Q进行射频指纹识别的深度神经网络模型进行了分类和讨论,并对射频指纹识别相关的开源数据集、数据表示方法和数据增强方法进行了整理和归纳。最后,该文讨论了基于深度学习的射频指纹识别方法所面临的难题和值得关注的研究方向,以期对射频指纹识别的研究与应用有所帮助。

     

  • 图  1  基于深度学习的射频指纹识别方法对比

    Figure  1.  Comparison of RFF methods based on deep learning

    图  2  基于Raw I/Q和深度学习的射频指纹识别方法分类

    Figure  2.  Classification of RFF methods based on Raw I/Q and deep learning

    图  3  使用Raw I/Q进行射频指纹识别的基础卷积神经网络模型

    Figure  3.  The basis CNN model inputted with Raw I/Q for RFF

    图  4  使用Raw I/Q进行射频指纹识别的ResNet模型

    Figure  4.  The ResNet model inputted with Raw I/Q for RFF

    图  5  使用Raw I/Q进行射频指纹识别的复数深度神经网络模型

    Figure  5.  The complex-valued DNN model inputted with Raw I/Q for RFF

    图  6  使用Raw I/Q进行射频指纹识别的循环神经网络模型

    Figure  6.  The RNN model inputted with Raw I/Q for RFF

    表  1  开源的射频指纹数据集

    Table  1.   Open source dataset of radio frequency fingerprint

    文献发射端环境、信道、采集时间等配置接收端
    辐射源数量信号类型接收机采样率其他
    [63]USRP X31016IEEE802.11a室内;LOS;不同距离(2~62 ft,
    6 ft为步长)
    USRP B2105 MS/s2e7个I/Q采样点/个体
    [57]USRP N210/X31020IEEE802.11a/g线缆直连/暗室/室内;不同时间(10 d);
    同一天线/不同天线
    USRP N21020 MS/s288个I/Q采样点/样本,
    250条样本/个体/配置,
    [81]USRP X3104IEEE802.11a /LTE/5G-NR室外;LOS/NLOS;不同时间(2 d);
    不同距离(300~1000 m)
    USRP B2105 MS/s,
    7.68 MS/s
    512个I/Q采样点/样本,
    3e6个I/Q采样点/个体
    [62]无线网卡174IEEE802.11a/g室内;LOS;不同时间(4 d);
    不同接收机(41个)
    USRP B210/
    N210/X310
    25 MS/s全集有1e7条数据包,1.4 TB大小,裁剪为4个子集
    [82]智能手机86BluetoothLOS;固定距离(30 cm)Tektronix TDS7404250 MS/s,
    5 GS/s
    150条样本/个体
    [83]飞行器140ADS-B真实飞行数据USRP B2108 MS/s总共3e4条样本
    [18]飞行器530/
    198
    ADS-B真实飞行数据Signal Hound
    SM200B
    50 MS/s200~600条样本/个体
    [84]物联网设备60LoRa室内;LOS/NLOS;静止/移动USRP N2101 MS/s8192个I/Q采样点/样本
    1000条样本/个体
    [85]物联网设备25LoRa室内/室外;不同时间(5 d);不同距离(5 m/10 m/15 m/20 m);不同接收机(2个)USRP B2101 MS/s2e8个I/Q采样点/个体/天
    [86]USRP 293221IEEE 802.15.4半电波暗室内;不同功率;
    受移动机器人扰动的动态信道
    USRP 29325 MS/s600个I/Q采样点/样本
    5e4条样本/个体
    [87]DJI M1007非标准波形暗室悬停;不同距离(6 ft/9 ft/12 ft/15 ft)USRP X31010 MS/s约92e3个I/Q采样点/样本
    2240条样本/个体
    下载: 导出CSV

    表  2  数据表示形式的研究

    Table  2.   Research of data representation

    文献网络模型信号类型设备数量待比较的数据
    表示形式
    研究结论
    [42]复数CNNWiFi/ADS-B100/1000I/Q, I/Q+FFTI/Q+FFT更好
    [90]LSTMWiFi4I/Q, NL, I/Q+NLI/Q+NL更稳定
    [51]有注意力机制CNNWiFi20I/Q, FFTFFT更好
    [91]复数CNNWiFi20I/Q, 差分I/Q差分I/Q更好
    [85]CNNLoRa25I/Q, FFT, $A/\phi $I/Q和$A/\phi $更好
    [26]CNNLoRa20I/Q, FFT, STFTSTFT更好。经频偏补偿后,3种表示形式时模型的
    正确率都获得大幅提升,STFT更好
    [50]CNN, LSTMLoRa100I/Q, $A/\phi $
    STFT
    <10个设备时,$ A/\phi $和STFT与LSTM模型配合最好;10~49个设备时,
    $ A/\phi $最好;50个设备以上时,3种数据表示形式都很差
    [66]CNN、复数CNNWired/WiFi/
    LoRa
    20/10/25I/Q, $A/\phi $实数CNN:LoRa数据集上,I/Q更好,Wired和WiFi数据集上,$ A/\phi $更好
    复数CNN:LoRa和WiFi数据集上,I/Q更好,Wired数据集上,$ A/\phi $更好
    下载: 导出CSV
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  • 收稿日期:  2022-07-07
  • 修回日期:  2022-10-10
  • 网络出版日期:  2022-10-26
  • 刊出日期:  2023-02-28

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