Leakage signal classification and recognition method based on fusion features
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摘要: 随着移动通信、物联网、车联网、工业互联网等网络的发展，电磁环境日益复杂，非法电子设备也日渐增多，各类信号耦合互调现象严重，这给泄漏信号类型识别带来了难题。提出基于融合特征的泄漏信号分类识别方法，综合运用高维度特征提取方法和图形化降维表征方法，结合残差网络等深度学习模型与特征融合分析方法，能够更综合地区分多类电磁泄漏信号，特征抗噪声鲁棒性高，方法可解释性好，可支撑基于电磁信号类型识别的辐射源智能检测工程应用。Abstract: With the development of networks such as mobile communications, Internet of Things (IoT), V2X ( meaning Vehicle to everything, including Vehicle to Vehicle and Vehicle to Infrastructure), and Industrial Internet of Things (IIoT), the electromagnetic environment is becoming increasingly complex, illegal electronic devices are also increasing day by day, and there are severe coupling and intermodulation of various signals, which bring difficulties to the identification of leaked signal types. This paper proposes a leakage signal classification and recognition method based on fused features. Comprehensively utilizing high-dimensional feature extraction methods and graphical dimensionality reduction characterization methods, and combining with deep learning models such as residual networks and feature fusion analysis methods, the method can distinguish more comprehensively multiple types of electromagnetic leakage signals. The features method has with high robustness against noise and good interpretability, and can support the intelligent detection engineering application of radiation sources based on electromagnetic signal type recognition.
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图 5 信噪比为0 dB时基于融合特征的预测结果混淆矩阵
Figure 5. Confusion matrix of prediction results based on fusion features when the signal-to-noise ratio is 0 dB
图 6 不同信噪比时基于小波特征的预测结果混淆矩阵
Figure 6. Confusion matrix of prediction results based on wavelet features at different signal-to-noise ratios
图 7 不同信噪比时基于HHT特征的预测结果混淆矩阵
Figure 7. Confusion matrix of prediction results based on HHT features at different signal-to-noise ratios
表 1 五类泄漏源
Table 1. Five types of leakage sources
No. signal type total sampling points
of each WAV filenumber of samples
intercepted by each WAV filetotal number of
samples taken1 clock leak signal 11264000，10035200， 7168000 ，7782400 563，501，358，389 1811 2 laptop touchpad leak signal 12247040，15589376， 17924096，21274624 612，779，896，1063 3350 3 environmental radio
emissions signal17981440，22003712，25976832， 25075712 ，15302656 899，1100，1298，1253，765 5315 4 screen display signal 21553152，34586624，26722304 1077，1729，1336 4142 5 unknown radiation source signal 15728640，17661952， 26402816，16826368 786，883，1320，841 3830 
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表 2 五类泄漏源特征
Table 2. Five types of leak source characteristics
No. signal type wavelet feature map HHT feature map bispectral feature map 1 clock leak signal 
2 laptop touchpad
leak signal
3 environmental radio
emissions signal
4 screen display signal 
5 unknown radiation
source signal
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表 3 五类泄漏源样本数据集数量
Table 3. Number of sample data sets of five types of leakage sources
No. signal type balanced dataset sample size unbalanced dataset sample size training set test set training set test set 1 clock leak signal 1440 360 1449 362 2 laptop touchpad leak signal 1440 360 2680 670 3 environmental radio emissions signal 1440 360 4252 1063 4 screen display signal 1440 360 3313 829 5 unknown radiation source signal 1440 360 3064 766
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表 4 不同信噪比下的不同特征图预测准确率
Table 4. Prediction accuracy of different feature maps under different signal-to-noise ratios
No. SNR/dB fusion feature map/% wavelet feature map/% HHT feature map/% bispectral feature map/% 1 0 99.8 95.8 95.2 100 2 3 100 98.4 97.8 100 3 5 100 93.6 98.8 100 4 7 100 93.0 99.8 100
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