CNN-Based SAR Automatic Target Recognition Using SAR Raw Data

Automatic target recognition (ATR) for synthetic aperture radar (SAR) has been a significant research topic for decades. Existing methods of ATR perform the task after image formation. However, the imaging process of target information may lose the target abstract feature information hidden in the original data. Motivated by this, we feed the SAR raw data to a deep convolutional neural network (CNN) that does not require image reconstruction. To evaluate the method, we trained two well-known CNN networks, VGG19 and ResNet18, on the simulated dataset and MSTAR dataset. The results show that both CNN network architectures can achieve good results for classification.