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Estimation of K-distribution parameters with application to target detection.

Marhaban, Mohammad Hamiruce. (2003) Estimation of K-distribution parameters with application to target detection. Doctoral thesis, University of Surrey (United Kingdom)..

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Abstract

Probabilistic models have been used extensively in the past to underpin classification algorithms in statistical pattern recognition. The most widely used model is the Gaussian distribution. However, signals of impulsive nature usually deviate from Gaussian and it is necessary to work with more realistic models. K-distribution is one of the long-tailed density which is known in the signal processing community for fitting the radar sea clutter accurately. The work presented in this thesis reflects the efforts made to model the background features, extracted from the sea images, by using a K-distribution. A novel approach for estimating the parameter of K-distribution is presented. The method utilises the empirical characteristic function, and is proven to perform better than any existing estimation technique. A classifier is then developed from the empirical characteristic function. This technique is applied to a problem of automatic target recognition with promising results.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors :
NameEmailORCID
Marhaban, Mohammad Hamiruce.
Date : 2003
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/THS
Additional Information : Thesis submitted for the Degree of Doctor of Philosophy, University of Surrey. Copyright remains with the author.
Depositing User : EPrints Services
Date Deposited : 09 Nov 2017 12:16
Last Modified : 20 Jun 2018 11:22
URI: http://epubs.surrey.ac.uk/id/eprint/844128

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