Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture | IEEE Journals & Magazine | IEEE Xplore

Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture


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. ...Show More

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.
Published in: IEEE Transactions on Aerospace and Electronic Systems ( Volume: 43, Issue: 2, April 2007)
Page(s): 775 - 779
Date of Publication: 08 August 2007

ISSN Information:


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