An Intelligent Method Based on Time–Frequency Analysis and Deep Learning Semantic Segmentation for Investigating the Electromagnetic Pulse Features of Engine Digital Controllers

A novel intelligent extraction method based on time–frequency analysis and deep learning semantic segmentation were proposed for detailing electromagnetic pulse (EMP) sensitive frequency bands of engine digital controllers. In this method, wavelet transformation was performed to convert EMP-coupled signals into image data sets. Two semantic segmentation networks, namely fully convolutional networks and DeepLabV3+, were used to train and verify the obtained image data sets. The performance of the two models was compared using the confusion matrix and Kappa coefficient. Threshold segmentation, binarization, and average pooling operations were sequentially performed on the feature map output of the networks, and EMP characteristic pixel percentage was calculated using the time–frequency map. The original image was segmented by using the result of the binarization of the feature map output by the last layer of the network as a mask. This technique is immune to the interference caused by noise pixels when calculating the spectrum index. The proposed method has improved the EMP feature extraction accuracy of the model, and the overall performance of the DeepLabV3+ network model is proper. It was performed to obtain a theoretical basis for subsequent targeted EMP protection.