Two-stage Networks with Adversarial Clutter Suppression for Maritime Radar Target Detection

Maritime radar target detection is often affected by sea clutter, and the detection performance in the case of low signal-to-clutter ratio (SCR) is usually poor. In this paper, we propose a two-stage deep learning method for sea clutter suppression and point target detection. Take the cluttered Range-Doppler (RD) spectra as input, at the first stage, reconstructed RD spectra are obtained as clutter suppression results through Attention Denoising Adversarial-Autoencoders (Atten-DAAE). At the second stage, detection results are obtained through the traditional one-stage detection network YOLOv5s. The proposed method has been verified on two datasets with simulated and measured clutter data respectively and compared with the traditional method and other networks, which shows better detection performance.