Radarda otomatik hedef sınıflandırma için yöntemler

Automatictarget recognition (ATR) using radar is an active research area. Inthis thesis, we develop new automatic radar target classificationmethods. We focus on two specific problems: (i) Synthetic ApertureRadar (SAR) target classification and (ii)Pulse-doppler radar (PDR)target classification. SAR and PDR target classification areextensively used for ground and battlefield surveillance tasks.In the first part of the thesis, a novel descriptive featureparameter extraction method from Synthetic Aperture Radar (SAR)images is proposed. Feature extraction and classification methodswhich were developed to handle optical images are usuallyinappropriate for SAR images because of the multiplicative nature ofthe severe speckle noise and imaging defects. In addition, SARimages of the same object taken at different aspect angles showgreat differences, which makes it hard to obtain satisfactoryresults. Consequently, feature parameter extraction method based ontwo-dimensional cepstrum is proposed and its object recognitionresults are compared with principal component analysis (PCA) andindependent component analysis (ICA) methods. The extracted featureparameters are classified using Support Vector Machines (SVMs).Experimental results are presented.In the second part of the thesis, the automatic classificationexperiments over ground surveillance Pulse-doppler radar echo signalare investigated in order to overcome the limitations of humanoperators and improve the classification accuracy. Covariance methodapproach is introduced for PDR echo signal classification. To thebest our knowledge, the use of covariance method-basedclassification is not investigated in radar automatic targetclassification problems. Furthermore, different approaches whichinvolves SVMs are developed. As feature parameters, cepstrum andMFCCs are used. Performances of these two approaches are comparedwith the Gaussian Mixture Models (GMM) based classification scheme.Experimental results and conclusions are presented.