Inverse Synthetic Aperture Radar Imaging Method for Convolutional Attention

In recent years, Convolutional Neural Network (CNN) has been used in Inverse Synthetic Aperture Radar (ISAR) due to its excellent feature extraction ability and data representation. However, standard CNNs treat all features equally, ignoring the importance of highlighting critical information and suppressing non-essential information, which often leads to limitations in imaging performance. In order to overcome this challenge, an ISAR imaging strategy integrating the Convolutional Block Attention Module (CBAM) mechanism has emerged. In this method, the convolutional attention module is embedded in the network architecture to deeply mine the details of features in the channel and spatial dimensions and enhance the representation ability of the network. In addition, it mitigates the effects of noise in the original feature in the residual connection, improving image clarity. The results show that the imaging technique using convolutional attention can generate high-resolution ISAR images, and its imaging performance is better than that of traditional fully convolutional neural network methods.