A Target Detection Method Based on Feature Transformation for Sea Clutter Suppression

To address the poor performance of conventional sea clutter suppression methods when target echoes share the same frequency as sea clutter, this paper proposes a suppression approach based on feature transformation. The autocorrelation matrix of sea clutter is first employed to construct a feature transformation matrix, through which the received radar data are normalized in the feature space. A differential operation is then applied to cancel clutter components, and the residual energy is combined with a cell-averaging constant false alarm rate (CA-CFAR) detector to achieve target detection in co-frequency scenarios. The proposed method requires no additional prior information and provides a new solution for detecting low-speed maritime targets. Simulation results demonstrate that the method significantly improves both signal-to-clutter ratio (SCR) and detection probability under various sea states and SCR conditions, with particularly notable advantages when the target frequency is close to the sea clutter spectral peak.