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dc.contributor.advisorÇetin, A. Enis
dc.contributor.authorEryıldırım, Abdülkadir
dc.date.accessioned2016-01-08T18:10:44Z
dc.date.available2016-01-08T18:10:44Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/11693/14903
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University., 2009.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 53-56.en_US
dc.description.abstractAutomatic target recognition (ATR) using radar is an active research area. In this thesis, we develop new automatic radar target classification methods. We focus on two specific problems: (i) Synthetic Aperture Radar (SAR) target classification and (ii)Pulse-doppler radar (PDR) target classification. SAR and PDR target classification are extensively used for ground and battlefield surveillance tasks. In the first part of the thesis, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. Feature extraction and classification methods which were developed to handle optical images are usually inappropriate for SAR images because of the multiplicative nature of the severe speckle noise and imaging defects. In addition, SAR images of the same object taken at different aspect angles show great differences, which makes it hard to obtain satisfactory results. Consequently, feature parameter extraction method based on two-dimensional cepstrum is proposed and its object recognition results are compared with principal component analysis (PCA) and independent component analysis (ICA) methods. The extracted feature parameters are classified using Support Vector Machines (SVMs). Experimental results are presented. In the second part of the thesis, the automatic classification experiments over ground surveillance Pulse-doppler radar echo signal are investigated in order to overcome the limitations of human operators and improve the classification accuracy. Covariance method approach is introduced for PDR echo signal classification. To the best our knowledge, the use of covariance method-based classification is not investigated in radar automatic target classification problems. Furthermore, different approaches which involves SVMs are developed. As feature parameters, cepstrum and MFCCs are used. Performances of these two approaches are compared with the Gaussian Mixture Models (GMM) based classification scheme. Experimental results and conclusions are presented.en_US
dc.description.statementofresponsibilityEryıldırım, Abdülkadiren_US
dc.format.extentxiii, 62 leaves, illustrations, graphicsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTarget classificationen_US
dc.subjectcovariance matrixen_US
dc.subjectregion covarianceen_US
dc.subjectGaussian Mixture Modelsen_US
dc.subjectSupport Vector Machineen_US
dc.subjectindependent component analysisen_US
dc.subjectprincipal component analysisen_US
dc.subjectfeature extractionen_US
dc.subjectradaren_US
dc.subject.lccTK6592.A9 E79 2009en_US
dc.subject.lcshAutomatic tracking.en_US
dc.subject.lcshRadar targets.en_US
dc.subject.lcshTracking radar.en_US
dc.subject.lcshOptical pattern recognition.en_US
dc.titleMethods fro automatic target classification in radaren_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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