高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

一种新的时分多址信号射频特征及其在特定辐射源识别中的应用

潘一苇 彭华 李天昀 王文雅

潘一苇, 彭华, 李天昀, 王文雅. 一种新的时分多址信号射频特征及其在特定辐射源识别中的应用[J]. 电子与信息学报, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163
引用本文: 潘一苇, 彭华, 李天昀, 王文雅. 一种新的时分多址信号射频特征及其在特定辐射源识别中的应用[J]. 电子与信息学报, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163
Yiwei PAN, Hua PENG, Tianyun LI, Wenya WANG. A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163
Citation: Yiwei PAN, Hua PENG, Tianyun LI, Wenya WANG. A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163

一种新的时分多址信号射频特征及其在特定辐射源识别中的应用

doi: 10.11999/JEIT190163
基金项目: 国家自然科学基金(61401511, U1736107)
详细信息
    作者简介:

    潘一苇:男,1990年生,博士生,研究方向为通信信号处理、特定辐射源识别

    彭华:男,1973年生,教授,研究方向为通信信号处理、软件无线电

    李天昀:男,1979年生,副教授,研究方向为通信信号处理、软件无线电

    王文雅:女,1991年生,硕士生,研究方向为通信信号处理、可见光通信

    通讯作者:

    潘一苇 novakd@163.com

  • 中图分类号: TN911.7

A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification

Funds: The National Natural Science Foundation of China (61401511, U1736107)
  • 摘要: 时分多址(TDMA)信号特定辐射源识别(SEI)的性能主要受限于突发数据的长度。为此,该文提出一种新的射频特征,从载波相位上揭示了相邻时隙的用户是否相同,为相同用户的数据累积提供了依据。该文首先分析了特征的产生机理,并给出了提取方法;根据特征的统计特性,推导了自适应的判决门限,实现了相邻时隙用户身份的检测;在此基础上,设计了新的SEI处理流程,通过数据累积打破了每个时隙单独识别的传统思维。实验结果表明:该特征对噪声具备良好的鲁棒性,能够实现相邻时隙用户身份的准确检测;与传统做法相比,新的处理流程能够有效改善TDMA信号SEI的性能。
  • 图  1  TDMA突发数据结构

    图  2  直接变频发射机的典型结构

    图  3  传统的TDMA信号SEI处理流程

    图  4  根据一段实际信号计算得到的升序向量

    图  5  新的TDMA信号SEI处理流程

    图  6  不同判决门限下的检测正确率

    图  7  不同信噪比下的漏检概率和虚检概率

    图  8  不同信噪比下有/无数据累积的识别正确率

    图  9  不同观测时长对算法性能的影响

    表  1  不同信噪比下的检测性能

    信噪比${{{E_S}} / {{N_0}}}$(dB)判决门限$\gamma $检测概率${P_{\rm{d}}}$(%)检测正确率${P_{\rm{c}}}$(%)
    100.058299.672397.2490
    120.046799.696997.6732
    140.038699.707798.0256
    160.033699.710898.2951
    180.030499.736998.4334
    200.028599.743198.5184
    220.027399.809298.6035
    240.026699.821598.6404
    下载: 导出CSV
  • DANEV B, ZANETTI D, and CAPKUN S. On physical-layer identification of wireless devices[J]. ACM Computing Surveys, 2012, 45(1): Article No.6. doi: 10.1145/2379776.2379782
    SPEZIO A E. Electronic warfare systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2002, 50(3): 633–644. doi: 10.1109/22.989948
    MERCHANT K, REVAY S, STANTCHEV G, et al. Deep learning for RF device fingerprinting in cognitive communication networks[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 160–167. doi: 10.1109/JSTSP.2018.2796446
    DING Lida, WANG Shilian, WANG Fanggang, et al. Specific emitter identification via convolutional neural networks[J]. IEEE Communications Letters, 2018, 22(12): 2591–2594. doi: 10.1109/LCOMM.2018.2871465
    KIM K, SPOONER C M, AKBAR I, et al. Specific emitter identification for cognitive radio with application to IEEE 802.11[C]. IEEE Global Telecommunications, New Orleans, USA, 2008: 1–5.
    HAN Jie, ZHANG Tao, REN Dongfang, et al. Communication emitter identification based on distribution of bispectrum amplitude and phase[J]. IET Science, Measurement & Technology, 2017, 11(8): 1104–1112. doi: 10.1049/iet-smt.2017.0024
    BERTONCINI C, RUDD K, NOUSAIN B, et al. Wavelet fingerprinting of radio-frequency identification (RFID) tags[J]. IEEE Transactions on Industrial Electronics, 2012, 59(12): 4843–4850. doi: 10.1109/TIE.2011.2179276
    ZHANG Jingwen, WANG Fanggang, DOBRE O A, et al. Specific emitter identification via Hilbert-Huang transform in single-hop and relaying scenarios[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(6): 1192–1205. doi: 10.1109/TIFS.2016.2520908
    任东方, 张涛, 韩洁, 等. 基于ITD与纹理分析的特定辐射源识别方法[J]. 通信学报, 2017, 38(12): 160–168. doi: 10.11959/j.issn.1000-436x.2017299

    REN Dongfang, ZHANG Tao, HAN Jie, et al. Specific emitter identification based on ITD and texture analysis[J]. Journal on Communications, 2017, 38(12): 160–168. doi: 10.11959/j.issn.1000-436x.2017299
    SATIJA U, TRIVEDI N, BISWAL G, et al. Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(3): 581–591. doi: 10.1109/TIFS.2018.2855665
    BRIK V, BANERJEE S, GRUTESER M, et al. Wireless device identification with radiometric signatures[C]. The 14th ACM International Conference on Mobile Computing and Networking, New York, USA, 2008: 116–127.
    PENG Linning, HU Aiqun, ZHANG Junqing, et al. Design of a hybrid RF fingerprint extraction and device classification scheme[J]. IEEE Internet of Things Journal, 2019, 6(1): 349–360. doi: 10.1109/JIOT.2018.2838071
    LIU Mingwei and DOHERTY J F. Specific emitter identification using nonlinear device estimation[C]. Proceedings of IEEE Sarnoff Symposium, Princeton, USA, 2008: 1–5.
    POLAK A C and GOECKEL D L. Wireless device identification based on RF oscillator imperfections[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(12): 2492–2501. doi: 10.1109/TIFS.2015.2464778
    ZHANG Yi. Wireless transmitter IQ balance and sideband suppression[EB/OL]. http://www.analog.com/media/en/technical-documentation/applicationnotes/AN-1100.pdf, 2018.
    潘一苇, 彭华, 李天昀, 等. 针对特定辐射源识别的高精度符号同步方法[J]. 通信学报, 2018, 39(8): 106–112. doi: 10.11959/j.issn.1000-436x.2018132

    PAN Yiwei, PENG Hua, LI Tianyun, et al. High-precision symbol timing algorithm for specific emitter identification[J]. Journal on Communications, 2018, 39(8): 106–112. doi: 10.11959/j.issn.1000-436x.2018132
    DE OLIVEIRA M C and BITMEAD R R. High-fidelity modulation parameter estimation of non-cooperative transmitters: Carrier frequency[J]. Digital Signal Processing, 2011, 21(5): 632–637. doi: 10.1016/j.dsp.2011.03.002
    CHANG C C and LIN C J. LIBSVM: A library for support vector machines[EB/OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2018.
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  4017
  • HTML全文浏览量:  1219
  • PDF下载量:  84
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-03-20
  • 修回日期:  2019-06-05
  • 网络出版日期:  2019-06-14
  • 刊出日期:  2019-11-01

目录

    /

    返回文章
    返回