Two novel methods for estimating the compound K-clutter parameters in presence of thermal noise

In this study, the authors present two novel methods to estimate the parameters of the compound K-clutter in presence of additive thermal noise. Based on the parametric fitting to the tail of the clutter distribution, the first method estimates simultaneously the unknown parameters. This is achieved by comparing the experimental cumulative distribution function, drawn from the recorded data intensity, to a set of curves derived from the mathematical model. To this effect, a multidimensional unconstraint non-linear algorithm; namely the Nelder-Mead method is used to minimise the residuals between the real data and the fitted curve with unknown parameters. Considering always the presence of thermal noise and based on the neuronal approaches and fuzzy inference systems, the second method also yields an accurate estimation and guarantees an inexpensive computation of the unknown parameters when the clutter-to-noise ratio (CNR), is known a priori. To assess the obtained results, the authors illustrate the effectiveness of these new methods through Monte-Carlo simulations.