Title :
Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture
Author :
Balleri, Alessio ; Nehorai, Arye ; Wang, Jiacheng
fDate :
4/1/2007 12:00:00 AM
Abstract :
The inverse gamma distributed texture is important for modeling compound-Gaussian clutter (e.g. for sea reflections), due to the simplicity of estimating its parameters. We develop maximum-likelihood (ML) and method of fractional moments (MoFM) estimates to find the parameters of this distribution. We compute the Cramer-Rao bounds (CRBs) on the estimate variances and present numerical examples. We also show examples demonstrating the applicability of our methods to real lake-clutter data. Our results illustrate that, as expected, the ML estimates are asymptotically efficient, and also that the real lake-clutter data can be very well modeled by the inverse gamma distributed texture compound-Gaussian model.
Keywords :
Gaussian distribution; Gaussian processes; clutter; gamma distribution; maximum likelihood estimation; Cramer-Rao bounds; compound-Gaussian clutter modeling; fractional moments estimates; inverse gamma distributed texture; maximum likelihood estimation; parameter estimation; real lake-clutter data; Inverse problems; Lakes; Maximum likelihood estimation; Parameter estimation; Radar clutter; Random processes; Reflection; Sea measurements; Speckle; Speech;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.2007.4285370