An End-to-End Sea Clutter Suppression Method Using Wavelet Convolution-Enhanced Attentional Complex-Valued Neural Network

Marine radar is widely employed in ocean monitoring systems. However, sea clutter significantly impairs radar data interpretability and degrades maritime target detection performance. Effective clutter suppression methods are thus essential to enhance target characteristics for improved detection. However, environmental sea clutter often exhibits complex statistical characteristics, causing traditional model-based methods to suffer from performance degradation. To address this challenge, this letter proposes a sea clutter suppression method based on a complex-valued neural network (CVNN). First, the network incorporates a wavelet convolution (WTConv) block to expand the receptive field. Second, complex-valued convolutional blocks integrated with an attention mechanism are designed to enhance latent feature extraction. Finally, the model’s performance is rigorously validated using real-measured data. Experimental results demonstrate that the proposed model achieves superior clutter suppression performance.