Specific Emitter Identification Using Signal Trajectory Image
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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%。
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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