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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