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A New Neural Network Based on CNN for EMIS Identification

verfasst von: Ying-chun Xiao, Feng Zhu, Sheng-xian Zhuang, Yang Yang

Erschienen in: Journal of Electronic Testing | Ausgabe 1/2022

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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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Titel: A New Neural Network Based on CNN for EMIS Identification
verfasst von: Ying-chun Xiao
Feng Zhu
Sheng-xian Zhuang
Yang Yang
Publikationsdatum: 08.03.2022
Verlag: Springer US
Erschienen in: Journal of Electronic Testing / Ausgabe 1/2022
Print ISSN: 0923-8174
Elektronische ISSN: 1573-0727
DOI: https://doi.org/10.1007/s10836-022-05985-1

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