Sea Clutter Suppression Method Based on Pyramid Multi-Scale Residual Attention Generative Adversarial Network

Sea clutter significantly impacts radar maritime target detection. Existing methods still show limited clutter suppression performance, and their generalization ability re quires further improvement. To address this issue, a sea clutter suppression method based on the pyramid multi-scale residual attention generative adversarial network (PMS-RA GAN) is proposed. In the generator, an integrated residual attention mechanism (IRAM) is designed to highlight the target and effectively suppress the background clutter. Furthermore, a multi-scale pyramid adaptive feature extraction (MPA) module based on sea clutter characteristics is designed to generate high quality radar time-frequency images. Additionally, a composite loss function is proposed to balance between image quality and detail performance. Experimental results on real world datasets demonstrate that PMS-RA GAN outperforms the existing methods in sea clutter suppression, significantly improving target detection performance. The complete code for the proposed PMS-RA GAN model is publicly available at https://github.com/202400358077/PMS-RA-GAN and archived on Zenodo at https://zenodo.org/records/19805662.