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Proceedings Paper

Optimum parameter estimate for K-distributed clutter using multiple moments
Author(s): Mohammed Jahangir; David Blacknell
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Paper Abstract

The authors analyze the sub-optimal performance of simple texture measures for estimating the reciprocal order parameter of K-distributed radar clutter. A non-committal neural net has been applied to the parameter estimation task which has shown that improved error estimates are obtained when multiple moments are used to characterize the texture. Prompted by this result a new estimator is proposed which combines the mean normalized log intensity and the amplitude contrast moments of the imaged data. Its error performance is determined by the relative weighting in which the two moments are combined. With an appropriate choice of the weighting the modified estimator outperforms the normalized log estimator and gives close to maximum likelihood performance on the estimates over a wide range of the parameters values which are of interest.

Paper Details

Date Published: 17 December 1996
PDF: 8 pages
Proc. SPIE 2958, Microwave Sensing and Synthetic Aperture Radar, (17 December 1996); doi: 10.1117/12.262723
Show Author Affiliations
Mohammed Jahangir, Defence Research Agency Malvern (United Kingdom)
David Blacknell, Defence Research Agency Malvern (United Kingdom)


Published in SPIE Proceedings Vol. 2958:
Microwave Sensing and Synthetic Aperture Radar
Giorgio Franceschetti; Christopher John Oliver; Franco S. Rubertone; Shahram Tajbakhsh, Editor(s)

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