High Power Laser and Particle Beams, Volume. 36, Issue 4, 043018(2024)
Leakage signal classification and recognition method based on fusion features
Figures & Tables(11)
Fig. 5. Confusion matrix of prediction results based on fusion features when the signal-to-noise ratio is 0 dB
Fig. 6. Confusion matrix of prediction results based on wavelet features at different signal-to-noise ratios
Fig. 7. Confusion matrix of prediction results based on HHT features at different signal-to-noise ratios
Table 1. Five types of leakage sources
View tableView in ArticleTable 1. Five types of leakage sources
No. signal type total sampling points of each WAV file number of samples intercepted by each WAV file total number of samples taken 1 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 signal 17981440，22003712，25976832， 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
Table 2. Five types of leak source characteristics
View tableView in ArticleTable 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
Table 3. Number of sample data sets of five types of leakage sources
View tableView in ArticleTable 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
Table 4. Prediction accuracy of different feature maps under different signal-to-noise ratios
View tableView in ArticleTable 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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Yunfeng Kou, Fei Dai, Zhiguo Zhao, Jianming Lü, Xie Ma. Leakage signal classification and recognition method based on fusion features[J]. High Power Laser and Particle Beams, 2024, 36(4): 043018
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Received: Jun. 19, 2023
Accepted: Aug. 29, 2023
Published Online: Apr. 22, 2024
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