Blind source separation of electromagnetic signals based on deep focusing U-Net

• Published in: Journal of intelligent & fuzzy systems 2023-11, Vol.45 (5), p.9157-9167
• Main Authors: yang, Chen, Jinming, Liu, Jian, Mao
• Format: Article
• Language: English
• Subjects: Deep learning; Electromagnetic radiation; Leakage; Neural networks; Noise reduction; Radiation; Supervised learning; Unsupervised learning
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-223568

The unintentional electromagnetic radiation of digital electronic devices during operation can cause information leakage and threaten the information security of the system. In order to explore the leakage level of important information, it is necessary to separate the electromagnetic leakage signal from the complex environmental electromagnetic wave, so the blind source separation technology is studied.Traditional blind source separation methods are mainly unsupervised learning methods, and their separation results are not as expected. In recent years, deep learning technology based on supervised learning has achieved good results in speech separation and other fields, indicating that it is a feasible method.In order to solve the problem of separating source signals from mixed electromagnetic radiation signals and reducing noise interference in electromagnetic safety detection. this paper proposes a Deep Focusing U-Net neural network, which makes the model pay more attention to the features at deeper layer. The network is applied to the blind separation of LCD electromagnetic leakage signals, and the good separation performance proves the effectiveness of this method.