Radar radiation source recognition method based on compressed residual network
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摘要: 针对低信噪比条件下，现有的雷达辐射源信号识别方法存在识别正确率低、时效性差的问题，提出了一种基于压缩残差网络的雷达辐射源信号识别方法。首先，利用Choi-Williams分布的时频分析方法将时域信号转换为二维时频图像；然后，根据应用场景特点，选择卷积神经网络（Convolutional Neural Networks, CNN）“压缩”范围；最后，构建压缩残差网络来自动提取图像特征并完成分类。仿真实验结果表明，在同等体量的设计下，与当前较为常用的标准CNN以及ResNet模型相比，所提模型能够降低信号识别运行时间约8.4倍，在信噪比为-14dB条件下对14种雷达辐射源信号的平均识别率高约5%。提供了一种高效的雷达辐射源信号智能识别方法，具有潜在的工程应用前景。Abstract: Aiming at the problems of low recognition accuracy and poor timeliness of existing radar emitter signal recognition methods under the condition of low SNR, this paper proposes a radar emitter signal recognition method based on compressed residual network. Using Choi-Williams distribution for reference, the time-domain signal is converted into a two-dimensional time-frequency image, which improves the effectiveness of signal essential feature extraction. According to the characteristics of the application scenario, select the “compression” range of convolutional neural networks (CNN), and build a compression residual network to automatically extract image features and identify. The simulation results show that compared with other advanced models, the proposed method can reduce the running time of signal recognition by about 8.4 times, and the average recognition rate of 14 radar emitter signals is at least 5% higher when the signal-to-noise ratio is −14 dB. This paper provides an efficient intelligent recognition method of radar emitter signal, which has potential engineering application prospects.
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图 1 14类雷达辐射源信号的CWD时频分布图
Figure 1. CWD time-frequency distribution of 14 radar emitter signals
图 7 不同方法对多相编码信号的识别率
Figure 7. Recognition rate of polyphase coded signals by different methods
图 8 不同方法识别多相编码信号的混淆矩阵
Figure 8. Confusion matrix for different methods to identify polyphase coding
表 1 信号参数设置
Table 1. Signal Parameter Setting
modulation type parameter ranges CW carrier frequency $ {f_0} $ [1/10，1/4]fs LFM
NLFMcarrier frequency $ {f_0} $ [1/10，1/4]fs bandwidth B [1/10，1/4]fs BPSK carrier frequency $ {f_0} $ [1/10，1/4]fs barker code length 7 QPSK carrier frequency $ {f_0} $ [1/10，1/4]fs phase coding sequence length 6 FMCW carrier frequency $ {f_0} $ [1/20，1/5]fs bandwidth B [1/20，1/5]fs cycle T 1$ {\text{μ s}} $ FRANK
P1、P2、P3、P4carrier frequency $ {f_0} $ [1/10，1/4]fs step frequency N {6，8} BFSK carrier frequency $ {f_0} $ [1/20，1/4]fs barker code length {11,13} QFSK carrier frequency $ {f_0} $ [1/10，1/4]fs frequency coding sequence length {6，7} COSTAS carrier frequency $ {f_0} $ [1/10，1/4]fs frequency coding sequence length {6，7，8} 
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表 2 不同模型各卷积运算需要的乘加计算量
Table 2. Multi-adds calculation amount required for each convolution operation of different models
model structure model 2 of this article FLOPS Ref. [22] model FLOPS conv1 112×112×1×32×7×7 19668992 112×112×1×32×7×7 19668992 conv2_x 56×56×32×32×3×3×2 57802752 56×56×32×32×3×3×2 57802752 conv3_x 28×28×32×64×3×3+28×28×64×64×3×3 43352064 28×28×32×64×3×3+28×28×64×64×3×3 43352064 conv4_x 14×14×64×128×3×3+14×14×128×128×3×3 43352064 28×28×64×128×3×3+28×28×128×128×3×3 173408256 conv5_x 7×7×128×256×3×3+7×7×256×256×3×3 43352064 28×28×128×256×3×3+28×28×256×256×3×3 693633024 conv6_x 3×3×256×256×3×3×2 10616832 28×28×256×256×3×3×2 924844032 fc 256×14 3584 256×14 3584 sum 218,148,352 1,912,712,704
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