Mixture of compound-Gaussian distributions for radar sea-clutter modeling

摘要

Compound Gaussian models are mostly considered for describing radar sea-clutter returns and are the basis of adaptive target detection with false alarm rate regulation. When high resolution radars operate at small grazing angles, the existing compound Gaussian distributions with additive thermal noise could not fit accurately the empirical data in some cases. This communication emphasises on the statistical description of the sea clutter using a mixture of compound inverse Gaussian (CIG) distribution, K distribution and generalized Pareto distribution (GP) with additive thermal noise. Non-linear least squares curve fitting technique based on the Nelder-Mead algorithm is used to find simultaneously optimal parameters values of the mixture model. Experiments comparisons are conducted to show a goodness of fit of the proposed mixture model for modelling the McMaster IPIX backscatter.