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基于矢量图的特定辐射源识别方法

潘一苇 杨司韩 彭华 李天昀 王文雅

潘一苇, 杨司韩, 彭华, 李天昀, 王文雅. 基于矢量图的特定辐射源识别方法[J]. 电子与信息学报, 2020, 42(4): 941-949. doi: 10.11999/JEIT190329
引用本文: 潘一苇, 杨司韩, 彭华, 李天昀, 王文雅. 基于矢量图的特定辐射源识别方法[J]. 电子与信息学报, 2020, 42(4): 941-949. doi: 10.11999/JEIT190329
Yiwei PAN, Sihan YANG, Hua PENG, Tianyun LI, Wenya WANG. Specific Emitter Identification Using Signal Trajectory Image[J]. Journal of Electronics and Information Technology, 2020, 42(4): 941-949. doi: 10.11999/JEIT190329
Citation: Yiwei PAN, Sihan YANG, Hua PENG, Tianyun LI, Wenya WANG. Specific Emitter Identification Using Signal Trajectory Image[J]. Journal of Electronics and Information Technology, 2020, 42(4): 941-949. doi: 10.11999/JEIT190329

基于矢量图的特定辐射源识别方法

doi: 10.11999/JEIT190329
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

    杨司韩:男,1990年生,硕士生,研究方向为通信信号处理、深度学习

    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

Specific Emitter Identification Using Signal Trajectory Image

Funds: The National Natural Science Foundation of China (61401511, U1736107)
  • 摘要:

    发射机的指纹特征具有复杂性,现有的认识水平制约了特定辐射源识别(SEI)的性能。为此,该文提出一种基于矢量图的SEI方法,应用深度学习技术实现了多种复杂特征的联合提取。该文首先分析了多种发射机畸变在矢量图上的视觉表现;在此基础上,以矢量图灰度图像作为信号表示,构建深度残差网络提取图像中的视觉特征。该方法克服了现有认知的局限,兼具高信息完整性和低计算复杂度。实验结果表明,与现有算法相比,该方法能够显著改善SEI的性能,识别增益约为30%。

  • 图  1  I/Q正交调制发射机

    图  2  I/Q调制器畸变的视觉表现

    图  3  滤波器畸变的视觉表现

    图  4  振荡器畸变的视觉表现

    图  5  功率放大畸变的视觉表现

    图  6  矢量图灰度图像

    图  7  深度残差网络的网络结构

    图  8  残差单元个数对识别性能的影响

    图  9  符号个数对识别性能的影响

    图  10  过采倍数对识别性能的影响

    图  11  不同算法的识别性能

    表  1  不同算法的复杂度对比

    算法文献[6]算法文献[8]算法文献[9]算法文献[10]算法文献[14]算法本文算法
    复杂度$O\left( {ML\lg \left( {ML} \right)} \right) + O\left( S \right)$$O\left( {ML} \right) + O\left( L \right)$$O\left( {ML} \right) + O\left( L \right)$$O\left( {ML} \right) + O\left( L \right)$$O\left( {PQ\lg Q} \right) + O\left( S \right)$$O\left( {ML} \right) + O\left( S \right)$
    下载: 导出CSV

    表  2  不同辐射源的畸变参数

    辐射源1234567
    $g$0.02990.01880.0081–0.0025–0.0128–0.0230–0.0329
    $\phi $0.01370.00930.00500.0006–0.0038–0.0081–0.0125
    ${c_{\rm I}}$0.01420.00970.00520.0007–0.0038–0.0083–0.0128
    ${c_{\rm Q}}$0.01470.01020.00570.0012–0.0033–0.0078–0.0123
    ${a_n}$–0.0640–0.0429–0.0218–0.00070.02040.04150.0627
    ${b_n}$–0.0740–0.0498–0.0256–0.00140.02280.04700.0713
    ${c_{\rm o}}$0.00020.00100.00180.00260.00340.00420.0050
    ${\lambda _3}$–0.2915–0.0079i–0.0003–0.0004i–0.4371–0.0092i–0.1459–0.0066i–0.5827–0.0096i–0.0731–0.0042i–0.3643–0.0085i
    ${\lambda _5}$0.0295+0.0005i0.0001+0.0004i0.0821+0.0048i0.0338+0.0014i0.0537+0.0029i0.0571+0.0035i0.0484+0.0022i
    下载: 导出CSV

    表  3  网络结构及其参数量和单批次训练时间

    RN246810
    conv17×7, 32, stride2
    max pool3×3, stride 2
    conv2_x$\left[ \begin{array}{l} 3 \times 3,32 \\ 3 \times 3,32 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,32 \\ 3 \times 3,32 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,32 \\ 3 \times 3,32 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,32 \\ 3 \times 3,32 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,32 \\ 3 \times 3,32 \\ \end{array} \right] \times 2$
    conv3_x$\left[ \begin{array}{l} 3 \times 3,64 \\ 3 \times 3,64 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,64 \\ 3 \times 3,64 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,64 \\ 3 \times 3,64 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,64 \\ 3 \times 3,64 \\ \end{array} \right] \times 2$
    conv4_x$\left[ \begin{array}{l} 3 \times 3,128 \\ 3 \times 3,128 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,128 \\ 3 \times 3,128 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,128 \\ 3 \times 3,128 \\ \end{array} \right] \times 2$
    conv5_x$\left[ \begin{array}{l} 3 \times 3,256 \\ 3 \times 3,256 \\ \end{array} \right] \times 2$$\left[ \begin{array}{l} 3 \times 3,256 \\ 3 \times 3,256 \\ \end{array} \right] \times 2$
    conv6_x$\left[ \begin{array}{l} 3 \times 3,512 \\ 3 \times 3,512 \\ \end{array} \right] \times 2$
    avg pool5-d fc, softmax
    参数量3.9×1041.7×1056.8×1052.7×1061.1×107
    训练时间 (s)0.35160.38580.40190.42620.4584
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-07
  • 修回日期:  2019-07-23
  • 网络出版日期:  2019-09-29
  • 刊出日期:  2020-06-04

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