SAR image generation method via improved CWGAN-GP

Traditional methods for generating Synthetic Aperture Radar (SAR) images often face challenges such as complex modeling, large computational demands, and long processing times. When dealing with non-cooperative targets, it is impossible to obtain accurate models and omnidirectional SAR images of the target, which greatly hinders subsequent research. This paper optimizes the loss function of the original CWGAN-GP network and proposes a feature encoding method to describe the azimuth features of targets in SAR images. Experiments generate 360 simulated SAR images with an interval of 1°, expanding the SAR image dataset. Finally, the expanded image dataset is used for training the recognition network. The improvement in recognition accuracy validates the feasibility of generating simulated SAR image data.