Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering

The detection performance of maritime radars is restricted by the unwanted sea echo or clutter which is the vector sum of scattering from the sea surface. The echo is noise-like and is expected from a set of randomly moving scatters. Although the number of these target-like data is small, they may c...

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Main Author:	Davari, Atefeh
Format:	Thesis
Language:	English Malay
Published:	2008
Subjects:	Parameter estimation Radar - Interference
Online Access:	http://psasir.upm.edu.my/id/eprint/7868/1/ABS---__FK_2008_59.pdf
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id	my-upm-ir.7868
record_format	uketd_dc
spelling	my-upm-ir.78682013-05-27T07:36:54Z Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering 2008 Davari, Atefeh The detection performance of maritime radars is restricted by the unwanted sea echo or clutter which is the vector sum of scattering from the sea surface. The echo is noise-like and is expected from a set of randomly moving scatters. Although the number of these target-like data is small, they may cause false alarm in maritime radar and perturb the target detection. K-distribution is known as the best fitted probability density function for the radar sea clutter. The accurate and fast parameter estimation of K-distribution for small number of sea clutter radar data is crucial task to avoid irreparable disasters. A novel approach to estimate the parameters of K-distribution based on fuzzy inference has been proposed in the thesis. Takagi-Sugeno Kang (TSK) model has been chosen since human knowledge is unavailable to be captured, whereas the sea clutter for specific parameter can be easily generated. GK- clustering has been used in order to identify the membership function of the antecedent parts. Least Square Method has been utilized to estimate the parameter of the K-distribution, which is represented in the consequent part of the fuzzy inference system. For a real-time implementation of the proposed method, vectorized programming technique has been implemented. In comparison with the conventional methods, this technique has less computational complexity, needs lesser time to train and estimates faster than any existing methods. Since the method is clustering based, some kind of pre-knowledge (rough estimation) is naturally stored in the structure of the TSK-fuzzy system and Least Square provides a mechanism to fine tune the consequent parameters. The novelty of the proposed method is the incorporation of the clustering (as a pre-estimator) with the estimation process. The resultant estimator then overcomes the bottleneck of the existing methods and is capable of handling even a small number of data. Parameter estimation Radar - Interference 2008 Thesis http://psasir.upm.edu.my/id/eprint/7868/ http://psasir.upm.edu.my/id/eprint/7868/1/ABS---__FK_2008_59.pdf application/pdf en public masters Universiti Putra Malaysia Parameter estimation Radar - Interference Faculty of Engineering Malay
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English Malay
topic	Parameter estimation Radar - Interference
spellingShingle	Parameter estimation Radar - Interference Davari, Atefeh Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering
description	The detection performance of maritime radars is restricted by the unwanted sea echo or clutter which is the vector sum of scattering from the sea surface. The echo is noise-like and is expected from a set of randomly moving scatters. Although the number of these target-like data is small, they may cause false alarm in maritime radar and perturb the target detection. K-distribution is known as the best fitted probability density function for the radar sea clutter. The accurate and fast parameter estimation of K-distribution for small number of sea clutter radar data is crucial task to avoid irreparable disasters. A novel approach to estimate the parameters of K-distribution based on fuzzy inference has been proposed in the thesis. Takagi-Sugeno Kang (TSK) model has been chosen since human knowledge is unavailable to be captured, whereas the sea clutter for specific parameter can be easily generated. GK- clustering has been used in order to identify the membership function of the antecedent parts. Least Square Method has been utilized to estimate the parameter of the K-distribution, which is represented in the consequent part of the fuzzy inference system. For a real-time implementation of the proposed method, vectorized programming technique has been implemented. In comparison with the conventional methods, this technique has less computational complexity, needs lesser time to train and estimates faster than any existing methods. Since the method is clustering based, some kind of pre-knowledge (rough estimation) is naturally stored in the structure of the TSK-fuzzy system and Least Square provides a mechanism to fine tune the consequent parameters. The novelty of the proposed method is the incorporation of the clustering (as a pre-estimator) with the estimation process. The resultant estimator then overcomes the bottleneck of the existing methods and is capable of handling even a small number of data.
format	Thesis
qualification_level	Master's degree
author	Davari, Atefeh
author_facet	Davari, Atefeh
author_sort	Davari, Atefeh
title	Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering
title_short	Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering
title_full	Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering
title_fullStr	Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering
title_full_unstemmed	Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering
title_sort	parameter estimation of k-distributed clutter based on fuzzy inference and gustafson-kessel clustering
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Engineering
publishDate	2008
url	http://psasir.upm.edu.my/id/eprint/7868/1/ABS---__FK_2008_59.pdf
_version_	1747810720769114112

Parameter Estimation of K-Distributed Clutter Based on Fuzzy Inference and Gustafson-Kessel Clustering

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