Maritime radars are widely used for surveillance, search and rescue and detection of vessels. However, target detection, especially in maritime environments, is often limited by sea clutter, which is produced by the radar returns of the continuously changing sea surface. Sea clutter changes with environmental conditions and radar configurations and may occupy similar regions in the Range-Doppler (RD) map as a moving target. As a result, the target can be masked by the clutter, making detection very challenging and generating a large number of false alarms. Although several classical and learning-based clutter suppression techniques have been proposed, they are often evaluated using different datasets and performance criteria, making
direct comparison difficult. In this thesis, classical and machine learning approaches for sea clutter suppression are evaluated under a common framework. A classical detector is first applied directly to RD maps to provide a detection baseline. Then a Selective Singular Value Decomposition (SVD) is used as a clutter suppression stage before Cell-Averaging Constant False Alarm Rate (CA-CFAR) detection. Then, the benefit of temporal information is also investigated by integrating consecutive RD maps. In parallel, a U-Net neural network is trained to reconstruct target only
representations from the original RD maps, while measurements with clutter only are also included to limit false target generation. Multi frame integration was also examined in the U-Net by stacking consecutive RD maps as separate input channels. All approaches are evaluated on a TNO experimental dataset using the same detection and clutter suppression metrics including target detection fraction, frame-level clutter breakthrough, signal-to-clutter ratio improvement and clutter suppression factor. The results show that Selective Singular Value Decomposition and temporal integration can improve target detection compared with CFAR detection alone, but the clutter breakthrough remained relatively high. On the contrary, the U-Net managed to have a much better balance between target detection and clutter breakthrough, especially when incorporating multiple frames. The three frame U-Net achieved the highest target detection fraction of approximately 94.2% and the four frame U-Net achieved the best balance with a target detection fraction over 90% and average clutter breakthrough 0.04 clusters per RD map. Overall, the results demonstrate that learning based sea clutter suppression is a promising alternative to
conventional maritime radar processing. ... Read more