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Erschienen in:

16.07.2024

Blind Source Separation of Electromagnetic Signals Based on Swish-Tasnet

verfasst von: Yang Chen, Jinming Liu, Jian Mao, Xiaoyu Pang

Erschienen in: Circuits, Systems, and Signal Processing | Ausgabe 10/2024

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Abstract

Digital devices may leak electromagnetic signals containing important information during operation, posing a risk of information leakage. In order to evaluate the safety level of the equipment, it is necessary to detect electromagnetic leakage signals and separate important information. Traditional methods such as ICA and IVA have limited performance when the signal input is smaller than the output. In recent years, signal separation technology based on deep learning has developed rapidly, demonstrating effective separation performance and potential in signal separation. For the separation of electromagnetic signals, we propose a blind source separation method for electromagnetic signals based on Swish-Tasnet. This method improves the time convolutional network structure and introduces the Swish activation function to optimize the internal functions and stacking layers of the model, thereby achieving efficient blind source separation of electromagnetic signals. The experiment evaluated the separation performance through SISDR, SDR, and SAR, and the results showed that this method performs excellently in reducing background noise interference and accurately separating signal sources from mixed signals. Swish-Tasnet not only provides a practical and effective technical means for electromagnetic signal leakage separation, but also brings substantial progress to the field of equipment information security assessment research. This method has broad prospects in practical applications and is worth further research and promotion.

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Metadaten
Titel
Blind Source Separation of Electromagnetic Signals Based on Swish-Tasnet
verfasst von
Yang Chen
Jinming Liu
Jian Mao
Xiaoyu Pang
Publikationsdatum
16.07.2024
Verlag
Springer US
Erschienen in
Circuits, Systems, and Signal Processing / Ausgabe 10/2024
Print ISSN: 0278-081X
Elektronische ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-024-02653-x