基于矢量圖的特定輻射源識別方法
doi: 10.11999/JEIT190329
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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
Specific Emitter Identification Using Signal Trajectory Image
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Institute of Information System Engineering, Information Engineering University, Zhengzhou 450001, China
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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%。
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關(guān)鍵詞:
- 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%.
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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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