GRNN-Based Outlier-Robust Parameter Estimation of Compound-Gaussian Sea Clutter With Generalized Inverse Gaussian Textures

In this letter, an outlier-robust estimation method using the generalized regression neural network (GRNN) and multiple sample percentiles and truncated moments is proposed to estimate the three parameters of the compound-Gaussian clutter model with generalized inverse Gaussian (CG-GIG) textures. The tri-parametric CG-GIG distributions include K-distributions, generalized Pareto (GP) intensity distributions, and CGIG distributions as biparametric subclasses and thus possess a strong ability to model various sea clutter data without type selection. It is shown that the ratios of the sample percentiles and truncated moments can uniquely be determined by the CG-GIG distribution, despite its overparameterization. The GRNN is trained to generate an approximate inverse function from the ratio vector to the CG-GIG distribution. By the trained GRNN, the GRNN-based outlier-robust parameter estimators are constructed. Experimental results using simulation data and measured sea clutter data show that the GRNN-based estimators outperform the moment-based estimators and the parametric curve fitting estimation (PCFE) method in performance.