Sea Clutter Influencing Factors Analysis and Parameter Estimation Based on Oceanographic Observations

Abstract

Accurate and robust sea clutter modeling and parameter estimation are foundational for target detection. Traditional modeling methods rely on measured data, while clutter modeling based on radar settings and oceanographic observations is an alternative. This letter leverages high-resolution sea clutter data from the Sea-Detecting Radar Data-Sharing Program (SDRDSP) to address this challenge. Three distribution types, which are generalized Pareto distribution (GPD), K distribution, and compound-Gaussian model with inverse Gaussian (CGIG), are considered. Using random forest (RF), we identify the most discriminative factors for distribution type classification: range and azimuth resolution cell (RARC), grazing angle, wave speed, wind speed, and significant wave height (SWH). Building on this, a stacking ensemble learning framework is proposed to effectively regress the shape and scale parameters from these optimized input features. Experiments validate the effectiveness of the proposed approach in distribution type classification and parameter estimation.