Operating modes identification of spaceborne SAR based on deep learning

An operating modes identification of spaceborne synthetic aperture radar (SAR) model based on one-dimensional convolutional neural network was proposed according to the timing characteristics of SAR signals, in order to solve the limitation of the recognition accuracy and the timeliness of traditional spaceborne SAR operating modes inversion methods. The impulse peak amplitude of the SAR signal was taken as input, more subtle and representative features of the original signal were learned by using adaptive learning and pattern recognition ability of the convolutional neural network, the human interference factors of traditional methods were avoided, and the effective identification of the operating modes of spaceborne SAR was finally realized. The one-dimensional convolutional neural network structure was designed referring to the existing convolutional neural network with good performance, and the better parameters were adjusted and set to train a model with good recognition performance according to the feedback of the accuracy and the loss value in the training process of the network. Contrast experiments based on simulation data demonstrate that the model has higher recognition accuracy than traditional spaceborne SAR operating modes inversion methods and has excellent robustness and noise anti-noise ability under different types of signals and different detection conditions. © 2022 Zhejiang University. All rights reserved.