Fast maximum-likelihood sea clutter parameter learning from the output of the envelope detector

We develop a fast learning technique to estimate the background statistics parameters from the output of the envelope detector, the inputs of which are multi-component Gaussian Mixture (GM) distributions. We use Fisher Scoring (FS) algorithm, which is Newton based and has fast convergence properties, to solve the log-likelihood minimization problem. Experimental results are given on real radar clutter data.