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A New Neural Network Based on CNN for EMIS Identification

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Abstract

Electromagnetic interference sources (EMIS) must be identified in order to locate them promptly. Because representative features of EMIS broadband signals are difficult to extract, we propose a new identification method based on convolutional neural network (CNN) to extract EMIS deep features from spectrum signals and increase recognition accuracy. To achieve noise reduction, we added a noise reduction layer (NRL) to the network, which uses background noise data as the weight to determine its correlation with the input data. Furthermore, a new loss function based on intra-class and inter-class relative distances is presented, which is paired with the Softmax loss function to make the network converge fast and consistently. Experiments on three data sets are used to validate the created method's overall performance. Simulated results demonstrate that the suggested method can effectively extract the deep features of the EMIS signal, enhance signal classification speed and accuracy, and achieve 100% accuracy on our data set.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Antonini G, Orlandi A (2001) Wavelet packet-based EMI signal processing and source identification. IEEE Trans Electromagn Compat 43(2):140–148. https://doi.org/10.1109/15.925533

    Article  Google Scholar 

  2. Bu K, He Y, Jing X, Han J (2020) Adversarial Transfer Learning for Deep Learning Based Automatic Modulation Classification. IEEE Signal Process Lett 27:880–884. https://doi.org/10.1109/LSP.2020.2991875

    Article  Google Scholar 

  3. Elsaadouny M, Barowski J, Rolfes I (2020) "Extracting the Features of the Shallowly Buried Objects using LeNet Convolutional Network," in Proceedings of 14th European Conference on Antennas and Propagation (EuCAP), Copenhagen, Denmark. 1–4. https://doi.org/10.23919/EuCAP.48036.2020.9135701

  4. Gao R, Li Z, Li HS, Ai B (2015) A Spectrum Sensing Method via Absolute Value Cumulating with Laplacian Noise. Wireless Pers Commun 83(2):1387–1404. https://doi.org/10.1007/s11277-015-2457-4

    Article  Google Scholar 

  5. Goodfellow I, Bengio Y, Courville A, Bach F (2016) Deep Learning. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  6. Hong Z et al (2019) Electromagnetic Pattern Extraction and Grouping for Near-Field Scanning of Integrated Circuits by PCA and K-Means Approaches. IEEE Trans Electromagn Compat 61(6):1811–1822. https://doi.org/10.1109/TEMC.2018.2890026

    Article  Google Scholar 

  7. Jiao Y, Han J, Xu B, Xiao M, Shen B, Sun H (2021) “Research on Domain Entity Extraction in Civil Aviation Safety,” in Proceedings IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). 384–388. https://doi.org/10.1109/ICCASIT53235.2021.9633439

  8. Kingma D, Ba J (2014) Adam: A method for stochastic optimization. Computer Science 12–22. http://arxiv.org/abs/1412.6980

  9. Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self normalizing neural networks. Adv Neural Inform Proc Sys 30:972–981. http://arxiv.org/abs/1706.02515

  10. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  11. Kulin M, Kazaz T, Moerman I, De Poorter E (2018) End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications. IEEE Access 6:18484–18501. https://doi.org/10.1109/ACCESS.2018.2818794

    Article  Google Scholar 

  12. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  13. Li D, Yang R, Li X, Zhu S (2020) Radar Signal Modulation Recognition Based on Deep Joint Learning. IEEE Access 8:48515–48528. https://doi.org/10.1109/ACCESS.2020.2978875

    Article  Google Scholar 

  14. Li H, Wang C, Zhao D (2015) An improved EMD and its applications to find the basis functions of EMI signals. Mathematical Problems in Eng 2015(150127). https://doi.org/10.1155/2015/150127

  15. Luo Y, Wong Y, Kankanhalli M, Zhao Q (2020) G-Softmax: Improving Intraclass Compactness and Interclass Separability of Features. IEEE Transactions on Neural Networks and Learning Systems 31(2):685–699. https://doi.org/10.1109/TNNLS.2019.2909737

    Article  Google Scholar 

  16. Nikdel S, Noh S, Shortle J (2021) “Common Cause Failure Analysis for Aviation Safety Assessment Models,” in Proceedings IEEE/AIAA 40th Digital Avionics Systems Conference (DASC). 1–10. https://doi.org/10.1109/DASC52595.2021.9594402

  17. O’Shea TJ, Roy T, Clancy TC (2018) Over-the-Air Deep Learning Based Radio Signal Classification. IEEE Journal of Selected Topics in Signal Processing 12(1):168–179. https://doi.org/10.1109/JSTSP.2018.2797022

    Article  Google Scholar 

  18. Railway applications – Electromagnetic compatibility – Part 2: Emission of the whole railway system to the outside world, EN50121–2

  19. Santamaría-Vázquez E, Martínez-Cagigal V, Vaquerizo-Villar F, Hornero R (2020) EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Basraed Bin-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 28(12):2773–2782. https://doi.org/10.1109/TNSRE.2020.3048106

    Article  Google Scholar 

  20. Sevgi L (2003) Complex electromagnetic problems and numerical simulation approaches. Wiley, Piscataway, New Jersey

    MATH  Google Scholar 

  21. Sheeny M, Wallace A, Wang S (2020) 300 GHz radar object recognition based on deep neural networks and transfer learning. IET Radar Sonar Navig 14(10):1483–1493. https://doi.org/10.1049/iet-rsn.2019.0601

    Article  Google Scholar 

  22. Shi D, Gao Y (2013) A method of identifying electromagnetic radiation sources by using support vector machines. China Communications 10(7):36–43. https://doi.org/10.1109/CC.2013.6570798

    Article  Google Scholar 

  23. Shieh CS, Lin CT (2002) A vector neural network for emitter identification. IEEE Trans Antennas Propag 50(8):1120–1127. https://doi.org/10.1109/TAP.2002.801387

    Article  Google Scholar 

  24. Sun S, Zhang T, Li Q, Wang J, Zhang W, Wen Z, Tang Y (2021) Fault Diagnosis of Conventional Circuit Breaker Contact System Based on Time-Frequency Analysis and Improved AlexNet. IEEE Trans Instrum Meas 70:1–12. https://doi.org/10.1109/TIM.2020.3045798

    Article  Google Scholar 

  25. Tian Y, Tatematsu A, Tanabe K, Miyajima K (2014) Development of Locating System of Pulsed Electromagnetic Interference Source Based on Advanced TDOA Estimation Method. IEEE Trans Electr Compatibility 56(6):1326–1334. https://doi.org/10.1109/TEMC.2014.2315438

  26. Van Der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 86:2579–2605

    MATH  Google Scholar 

  27. Wang F, Gong W, Liu J, Wu K (2020) Channel Selective Activity Recognition with WiFi: A Deep Learning Approach Exploring Wideband Information. IEEE Transactions on Network Science and Engineering 7(1):181–192. https://doi.org/10.1109/TNSE.2018.2825144

    Article  Google Scholar 

  28. West NE, O'Shea T (2017) "Deep architectures for modulation recognition," in Proceedings of 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, USA. 1–6. https://doi.org/10.1109/DySPAN.2017.7920754

  29. Wickramasinghe CS, Marino DL, Manic M (2021) ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation. IEEE Access 9:40511–40520. https://doi.org/10.1109/ACCESS.2021.3064819

    Article  Google Scholar 

  30. Willson GB (1990) “Radar classification using a neural network,” in Proceedings of Applications of Artificial Neural Networks. Bellingham, WA: SPIE. 200–210

  31. Xiao C, Zhao T (2017) Identification Method of EMI Sources Based on Measured Single-Channel Signal and its Application in Aviation Secondary Power Source Design. IEEE Trans Electromagn Compat 59(2):439–446. https://doi.org/10.1109/TEMC.2016.2612704

    Article  Google Scholar 

  32. Yang J, Yang JY (2003) Why can LDA be performed in PCA transformed space? Pattern Recognition. 36(2):563–566. https://doi.org/10.1016/S0031-3203(02)00048-1

  33. Ye X, Zhu Q (2019) Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers. IEEE Access 7:42024–42031. https://doi.org/10.1109/ACCESS.2019.2904614

    Article  Google Scholar 

  34. Zhang F, Hu C, Yin Q, Li W, Li H, Hong W (2017) Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition. IEEE Access 5:26880–26891. https://doi.org/10.1109/ACCESS.2017.2773363

  35. Zhou Y, Chang H, Lu Y, Lu X, Zhou R (2021) Improving the Performance of VGG Through Different Granularity Feature Combinations. IEEE Access 9:26208–26220. https://doi.org/10.1109/ACCESS.2020.3031908

    Article  Google Scholar 

  36. Zhao M, Zhong S, Fu X, Tang B, Pecht M (2020) Deep Residual Shrinkage Networks for Fault Diagnosis. IEEE Trans Industr Inf 16(7):4681–4690. https://doi.org/10.1109/TII.2019.2943898

    Article  Google Scholar 

  37. Zhao X, Wen Z, Pan X, Ye W, Bermak A (2019) Mixture Gases Classification Based on Multi-Label One-Dimensional Deep Convolutional Neural Network. IEEE Access 7:12630–12637. https://doi.org/10.1109/ACCESS.2019.2892754

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Key R&D Program of China (No. 2018YFC0809500).

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Authors and Affiliations

  1. Southwest Jiaotong University, Chengdu, 611756, China

    Ying-chun Xiao, Feng Zhu, Sheng-xian Zhuang & Yang Yang

  2. Lanzhou City University, Lanzhou, 730070, China

    Ying-chun Xiao

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Correspondence to Feng Zhu.

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Xiao, Yc., Zhu, F., Zhuang, Sx. et al. A New Neural Network Based on CNN for EMIS Identification. J Electron Test 38, 77–89 (2022). https://doi.org/10.1007/s10836-022-05985-1

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