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In this correspondence, fractional negative-order moments are used to estimate the K-clutter plus noise parameters. Combining the fractional positive and negative moments, we aim to improve the accuracy of the estimation outcomes. This is achieved through the reduction of the effect of the two first intensity moments involved in the equation of the unknown parameters. For instance, the proposed fractional positive- and negative-order moments? estimator, given in terms of two confluent hypergeometric functions, is solved numerically to yield the shape parameter. Using single pulse and multiple pulses in various clutter plus noise scenarios, we show, via both simulated and IPIX real data, a comparison of the new estimator with the estimators based on the higher-order moments, the fractional positive-order moments, the [zlog(z)], and the constrained maximum likelihood. [ABSTRACT FROM PUBLISHER]

The estimation of the K-distributed radar clutter plus thermal noise parameters can be improved by employing fractional moments instead of the existing higher order moments. In order to achieve this, we provide first an asymptotic expression of the moments in terms of the confluent and Gauss hypergeometric functions. For fractional orders of the latter, the new estimation method is derived so that the estimates of the shape parameter are achieved via numerical computations of nonlinear equations. For lower fractional orders of moments and different values of the parameters, the numerical simulations demonstrate the effectiveness of the proposed estimation procedure when compared with the standard higher order moments method. [ABSTRACT FROM AUTHOR]

A statistical model for high-resolution sea clutter, which we have called the compound inverse Gaussian (CIG) distribution, is proposed. The model is a mixture of the Rayleigh distribution and the inverse Gaussian (IG) distribution to model the speckle and the texture components, respectively. The CIG probability density function (pdf) is generalized to account for the additive thermal noise to achieve a good match to real data. The overall pdf is given in an integral form as a function of three parameters which are estimated from the recorded data based on the parametric curve-fitting estimation (PCFE) method of the complementary cumulative distributed function (CCDF). The Nelder-Mead (N-M) simplex algorithm is used to provide the best estimates of the pdf parameters. Using the IPIX backscatter database, the fitting of the CIG pdfs and the cumulative distributed functions (cdfs) are assessed and compared with the fitted Weibull, log-normal, Rician inverse Gaussian (RiIG), K plus noise, compound log-normal (CLN), and Pareto plus noise distributions. Experimental fitting results show that the sea-clutter amplitudes obey the proposed CIG model in most cases. [ABSTRACT FROM AUTHOR]