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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
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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

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