Robust Sea Clutter Suppression by C-ASJU Dictionary Learning and ECWT Denoised Algorithm

To address the challenge of detecting weak targets obscured by strong sea clutter and noise in complex maritime environments, this paper presents a robust sea clutter suppression method. It combines a complex-adaptive sequential joint update (C-ASJU) dictionary learning algorithm with an enhanced continuous wavelet threshold (ECWT) denoising technique. First, the proposed dictionary learning (DL) and sparse representation (SR) are utilized to differentiate between sea clutter and target signals. The ASJU dictionary learning algorithm employs rank-1 matrix approximation and adaptive sparse regularization to balance computational efficiency and accuracy. By extending the method to the complex domain, the proposed C-ASJU algorithm can process complex sea clutter signals directly and preserve the phase information by using the Wirtinger calculus, thereby enhancing its applicability in real-time radar systems. To further adapt to diverse oceanic environments, an enhanced continuous wavelet threshold denoising method is integrated to effectively suppress the residual noise and enhance target detection efficiency. Compared with traditional algorithms and other DL algorithms, this method excels in clutter suppression performance, computational efficiency, and significantly boosts weak target detection in sea clutter. Experimental results on the IPIX radar datasets demonstrate the robustness and reliability of the proposed method.