A Weak Signal Detection Method Based on HFER Features in Sea Clutter Background

To address the issue of aliasing between weak signals and sea clutter, we have developed a weak signal detection method leveraging High-Frequency Energy Ratio (HFER) features. This feature detection approach significantly enhances the detection performance of weak signals against the backdrop of sea clutter. By thoroughly examining the echo characteristics that distinguish clutter range gates from target range gates, we transition the analysis from the observation domain to the feature domain, thereby achieving effective discrimination between the two. We analyze the distribution characteristics of high-frequency IMF energy ratios following CEEMD decomposition and construct a weak signal detection network using XGBoost, with the energy ratio as the key feature. The hyperparameters of the network are optimized using the Sparrow Search Algorithm (SSA). We conducted a comparative analysis using the BCD, RAA, TIE, SVM, and multi-feature fusion detection methods. The experimental results showed that the detection probability of the proposed method can reach over 95%, significantly improving the sea surface monitoring and target tracking capabilities of sea radar.