Modelling of sea clutter with Gaussian mixtures and estimation of the clutter parameter

In order to model the sea clutter we propose to use a generalized distribution, which is valid for different background statistics like low and high sea states. Envelope detector's input statistics are assumed to be a Gaussian-Mixture (GM). New background probability density function (pdf) resemble Rician Mixtures. In order to detect targets using adaptive techniques, clutter background statistics should be known a priori. But in practice, background statistics are typically not known a priori. Thus, a joint estimation and detection process is employed whereby the background statistics are estimated before target detection. We estimate the parameters using a Maximum Likelihood (ML) based method from the output of the envelope detector. Performance analysis is presented using real sea clutter data.