A model for non Rayleigh sea clutter amplitudes using compound Inverse Gaussian distribution

Abstract

Statistical model for high resolution sea clutter, which we called the compound Inverse Gaussian distribution (CIG), is proposed. The model is a mixture of the Rayleigh distribution and the Inverse Gaussian distribution to model the speckle and the texture components respectively. The proposed distribution is extended to cover the additive thermal noise to achieve a good match to real data. The overall amplitude distribution is given in an integral form as a function of three parameters which are estimated from the recorded data based on the curve fitting method of the cumulative distributed function (CDF). The Nelder-Mead (N-M) optimizer is used to provide the best estimates of these parameters. Based on the mean square error (MSE) of model estimates criterion, we compare the goodness of fit of the proposed compound IG distribution with that of the conventional models such as Weibull, Log-normal, compound K and Rician Inverse Gaussian (RiIG) distributions. It is shown that the proposed model turns out the best statistical model in all cases.