PARAMETER ESTIMATION OF COMPOUND-GAUSSIAN CLUTTER WITH NAKAGAMI-DISTRIBUTED TEXTURE

Abstract

This paper addresses the parameter estimation of the compound-Gaussian clutter with Nakagami texture (CGNG). The CGNG distribution was recently introduced to model high-resolution sea clutter. Two estimators are proposed: the fractional-order moment estimator (FOME) and the fractional-negative-order moment estimator (FNOME). The estimation performance of the proposed estimators is assessed and compared with that of the existing higher-order moment methods (HOME) and the [zlog(z)] estimator. Using both simulated and real data, estimation accuracy and modeling performance are evaluated using the chi-squared test (χ2) and the mean square error (MSE).