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

doi:10.11999/JEIT190329

基于矢量图的特定辐射源识别方法

doi: 10.11999/JEIT190329

战略支援部队信息工程大学信息系统工程学院郑州 450001

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
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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

Specific Emitter Identification Using Signal Trajectory Image

Institute of Information System Engineering, Information Engineering University, Zhengzhou 450001, China

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

摘要

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%。
- 特定辐射源识别 /
- 矢量图 /
- 深度残差网络 /
- 视觉特征 /
- 信息完整性
Abstract:
The radio frequency fingerprinting of the emitter is complex, and the performance of Specific Emitter Identification (SEI) is subjected to the present expertise. To remedy this shortcoming, this paper presents a novel SEI algorithm based on signal trajectory image, which realizes joint extraction of multiple complex fingerprints using deep learning architecture. First, this paper analyses the visual characteristics of multiple emitter imperfections in the signal trajectory image. Thereafter, signal trajectory grayscale image is used as the signal representation. Finally, a deep residual network is constructed to learn the visual characteristics reflected in the images. The proposed method overcomes the limitations of existing knowledge, and combines high information integrity with low computational complexity. Simulation results demonstrate that, compared with the existing algorithms, the proposed one can remarkably improve the SEI performance with a gain of about 30%.
- Specific Emitter Identification (SEI) /
- Signal trajectory image /
- Deep residual network /
- Visual characteristic /
- Information integrity

HTML全文

图 1 I/Q正交调制发射机

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图 2 I/Q调制器畸变的视觉表现

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图 3 滤波器畸变的视觉表现

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图 4 振荡器畸变的视觉表现

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图 5 功率放大畸变的视觉表现

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图 6 矢量图灰度图像

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图 7 深度残差网络的网络结构

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图 8 残差单元个数对识别性能的影响

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图 9 符号个数对识别性能的影响

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图 10 过采倍数对识别性能的影响

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图 11 不同算法的识别性能

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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	$O\left( {ML\lg \left( {ML} \right)} \right) + O\left( S \right)$	$O\left( {ML} \right) + O\left( L \right)$	$O\left( {ML} \right) + O\left( L \right)$	$O\left( {ML} \right) + O\left( L \right)$	$O\left( {PQ\lg Q} \right) + O\left( S \right)$	$O\left( {ML} \right) + O\left( S \right)$

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表 2 不同辐射源的畸变参数

辐射源	1	2	3	4	5	6	7
$g$	0.0299	0.0188	0.0081	–0.0025	–0.0128	–0.0230	–0.0329
$\phi $	0.0137	0.0093	0.0050	0.0006	–0.0038	–0.0081	–0.0125
${c_{\rm I}}$	0.0142	0.0097	0.0052	0.0007	–0.0038	–0.0083	–0.0128
${c_{\rm Q}}$	0.0147	0.0102	0.0057	0.0012	–0.0033	–0.0078	–0.0123
${a_n}$	–0.0640	–0.0429	–0.0218	–0.0007	0.0204	0.0415	0.0627
${b_n}$	–0.0740	–0.0498	–0.0256	–0.0014	0.0228	0.0470	0.0713
${c_{\rm o}}$	0.0002	0.0010	0.0018	0.0026	0.0034	0.0042	0.0050
${\lambda _3}$	–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+0.0005i	0.0001+0.0004i	0.0821+0.0048i	0.0338+0.0014i	0.0537+0.0029i	0.0571+0.0035i	0.0484+0.0022i

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表 3 网络结构及其参数量和单批次训练时间

RN	2	4	6	8	10
conv1	7×7, 32, stride2
max pool	3×3, 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 pool	5-d fc, softmax
参数量	3.9×10⁴	1.7×10⁵	6.8×10⁵	2.7×10⁶	1.1×10⁷
训练时间 (s)	0.3516	0.3858	0.4019	0.4262	0.4584

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