基于矢量圖的特定輻射源識別方法

潘一葦; 楊司韓; 彭華; 李天昀; 王文雅

doi:10.11999/JEIT190329

基于矢量圖的特定輻射源識別方法

doi: 10.11999/JEIT190329

戰(zhàn)略支援部隊信息工程大學(xué)信息系統(tǒng)工程學(xué)院鄭州 450001

基金項目: 國家自然科學(xué)基金 (61401511, U1736107)

詳細信息

作者簡介:
潘一葦：男，1990年生，博士生，研究方向為通信信號處理、特定輻射源識別

楊司韓：男，1990年生，碩士生，研究方向為通信信號處理、深度學(xué)習(xí)

彭華：男，1973年生，教授，研究方向為通信信號處理、軟件無線電

李天昀：男，1979年生，副教授，研究方向為通信信號處理、軟件無線電

王文雅：女，1991年生，碩士生，研究方向為通信信號處理、可見光通信

通訊作者:
潘一葦　novakd@163.com

中圖分類號: TN911.7
計量
- 文章訪問數(shù): 3918
- HTML全文瀏覽量: 1352
- PDF下載量: 185
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-05-07
- 修回日期: 2019-07-23
- 網(wǎng)絡(luò)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:
發(fā)射機的指紋特征具有復(fù)雜性，現(xiàn)有的認識水平制約了特定輻射源識別(SEI)的性能。為此，該文提出一種基于矢量圖的SEI方法，應(yīng)用深度學(xué)習(xí)技術(shù)實現(xiàn)了多種復(fù)雜特征的聯(lián)合提取。該文首先分析了多種發(fā)射機畸變在矢量圖上的視覺表現(xiàn)；在此基礎(chǔ)上，以矢量圖灰度圖像作為信號表示，構(gòu)建深度殘差網(wǎng)絡(luò)提取圖像中的視覺特征。該方法克服了現(xiàn)有認知的局限，兼具高信息完整性和低計算復(fù)雜度。實驗結(jié)果表明，與現(xiàn)有算法相比，該方法能夠顯著改善SEI的性能，識別增益約為30%。
- 特定輻射源識別 /
- 矢量圖 /
- 深度殘差網(wǎng)絡(luò) /
- 視覺特征 /
- 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

圖 1 I/Q正交調(diào)制發(fā)射機

下載: 全尺寸圖片幻燈片

圖 2 I/Q調(diào)制器畸變的視覺表現(xiàn)

下載: 全尺寸圖片幻燈片

圖 3 濾波器畸變的視覺表現(xiàn)

下載: 全尺寸圖片幻燈片

圖 4 振蕩器畸變的視覺表現(xiàn)

下載: 全尺寸圖片幻燈片

圖 5 功率放大畸變的視覺表現(xiàn)

下載: 全尺寸圖片幻燈片

圖 6 矢量圖灰度圖像

下載: 全尺寸圖片幻燈片

圖 7 深度殘差網(wǎng)絡(luò)的網(wǎng)絡(luò)結(jié)構(gòu)

下載: 全尺寸圖片幻燈片

圖 8 殘差單元個數(shù)對識別性能的影響

下載: 全尺寸圖片幻燈片

圖 9 符號個數(shù)對識別性能的影響

下載: 全尺寸圖片幻燈片

圖 10 過采倍數(shù)對識別性能的影響

下載: 全尺寸圖片幻燈片

圖 11 不同算法的識別性能

下載: 全尺寸圖片幻燈片

表 1 不同算法的復(fù)雜度對比

算法	文獻[6]算法	文獻[8]算法	文獻[9]算法	文獻[10]算法	文獻[14]算法	本文算法
復(fù)雜度	$O\left( {ML\lg \left( {ML} \right)} \right) + O\left( S \right)$	$O\left( {ML} \right) + O\left( L \right)$	$O\left( {ML} \right) + O\left( L \right)$	$O\left( {ML} \right) + O\left( L \right)$	$O\left( {PQ\lg Q} \right) + O\left( S \right)$	$O\left( {ML} \right) + O\left( S \right)$

下載: 導(dǎo)出CSV

表 2 不同輻射源的畸變參數(shù)

輻射源	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

下載: 導(dǎo)出CSV

表 3 網(wǎng)絡(luò)結(jié)構(gòu)及其參數(shù)量和單批次訓(xùn)練時間

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
參數(shù)量	3.9×10⁴	1.7×10⁵	6.8×10⁵	2.7×10⁶	1.1×10⁷
訓(xùn)練時間 (s)	0.3516	0.3858	0.4019	0.4262	0.4584

下載: 導(dǎo)出CSV

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