## 1 Introduction  
Understanding the characteristics of sea clutter is crucial for designing effective radar target-detection algorithms. The statistical properties of sea clutter play a significant role in determining the constant false alarm characteristics of target detection [1; 2; 3]. Specifically, the collected sea clutter data from shore-based radar exhibits a pronounced long trailing behavior in its statistical distribution [4; 5; 6; 7; 8], commonly referred to as the phenomenon of heavy trailing. The composite Gaussian model is well-suited for capturing the heavy trailing characteristics observed in sea clutter data [9; 10; 11]. This model comprises a slow-varying structural component that modulates a fast-varying scattering component [12], providing a plausible explanation for the formation mechanism of sea clutter. Depending on the composition of the texture components, three distinct statistical distributions are commonly used, namely the K distribution [13], generalized Pareto distribution [14], and IG-CG distribution [15]. The probability density function of the K distribution can be regarded as the product of the texture component and the speckle component of sea clutter, with the texture component following a gamma distribution. In contrast, the texture component of the generalized Pareto distribution follows an inverse gamma distribution, while the IG-CG is a compound Gaussian distribution model with an inverse Gaussian texture. Each distribution model is associated with its optimal detectors, and accurate parameter estimation plays a pivotal role in determining the performance of these detectors. Hence, precise parameter estimation is of utmost importance for the effective application of distribution models. Parameter estimation methods for the generalized Pareto distribution can be categorized into moment estimation [16; 17; 18; 19], maximum likelihood estimation [20; 21], and quantile parameter estimation [22; 23]. Moment estimation involves estimating integer order moments, fractional order moments, logarithmic moments, and other variants. The accuracy of parameter estimation varies across different moment estimation methods, with challenges such as non-closed expressions and a limited range for shape parameter estimation. Maximum likelihood estimation provides the highest accuracy but involves computationally intensive nonlinear expression solutions [24; 25]. Sub-locality-based parameter estimation methods effectively mitigate estimation errors caused by anomalous clutter data samples [23]. However, their utilization of echo data information is limited, and practical application often requires combining estimation results from multiple sub-locality points, thereby increasing computational complexity. [PERSON] et al. [26] proposed a multi-scan recursive Bayesian estimation method for the parameter estimation of the generalized Pareto distribution in large-scale sea clutter scenes, which demonstrated convergence and robustness. [PERSON] et al. [20] addressed sea clutter modeling with outliers using the generalized Pareto distribution and proposed an iterative algorithm to efficiently solve the truncated maximum likelihood equation. [PERSON] et al. [23] employed a double-percentile parameter estimation method for effective parameter estimation of the generalized Pareto distribution measured sea clutter waves, providing an analysis of its effectiveness. Solving non-closed expressions poses challenges for parameter estimators in practical applications of the generalized Pareto distribution. These non-closed expressions can be treated as nonlinear optimization problems of the objective function, and population intelligence search methods from the field of artificial intelligence [27; 28] offer common approaches for solving such problems. The application of artificial intelligence methods to parameter estimation of sea clutter distribution models is a relatively new research direction [29].