Sea Clutter Suppression for Maritime Radar Measurements With MPDNet

Maritime radar systems play a critical role in ocean surveillance and navigation; however, the presence of sea clutter significantly degrades measurement performance. Traditional sea clutter suppression methods often struggle with the complexity and non-stationary characteristics of sea clutter, especially under harsh maritime conditions. Therefore, a framework based on multi-path decoder network (MPDNet) is proposed to achieve efficient sea clutter suppression. MPDNet integrates a multipath feature extraction and fusion strategy. It incorporates innovative intermediate modules such as Mamba2Block and KNNLayer, as well as decoder modules including Pinwheel-shaped convolution and TiedBlockConv2d. Furthermore, a multi-decoder architecture is designed, together with a multi-objective loss joint optimization strategy to balance the contributions of each path and improve clutter suppression performance. This architecture allows the model to capture both global and local features while maintaining computational efficiency. Experimental results on six real-world radar datasets show that the lightweight MPDNet outperforms existing networks in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the P-S metric. Ablation studies further validate the effectiveness of using three encoder branches over other configurations. In the target detection stage, YOLOv8 achieves an inference time reduced to approximately one-fifth of that under the original clutter background after processing with the proposed method, along with improved detection accuracy. These results demonstrate good generalization performance and further verify the effectiveness of MPDNet in sea clutter suppression.