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dc.contributor.advisorBarshan, Billur
dc.contributor.authorEravcı, Bahaeddin
dc.date.accessioned2016-01-08T18:13:57Z
dc.date.available2016-01-08T18:13:57Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/11693/15133
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2010.en_US
dc.descriptionIncludes bibliographical references leaves 63-65.en_US
dc.description.abstractEstimation of the radar antenna scan period and recognition of the antenna scan type is usually performed by human operators in the Electronic Warfare (EW) world. In this thesis, we propose a robust algorithm to automatize these two critical processes. The proposed algorithm consists of two main parts: antenna scan period estimation and antenna scan type classification. The first part of the algorithm involves estimating the period of the signal using a time-domain approach. After this step, the signal is warped to a single vector with predetermined size (N) by resampling the data according to its period. This process ensures that the extracted features are reliable and are solely the result of the different scan types, since the effect of the different periods in the signal is removed. Four different features are extracted from the signal vector with an understanding of the phenomena behind the received signals. These features are used to train naive Bayes classifiers, decision-tree classifiers, artificial neural networks, and support vector machines. We have developed an Antenna Scan Pattern Simulator (ASPS) that simulates the position of the antenna beam with respect to time and generates synthetic data. These classifiers are trained and tested with the synthetic data and are compared by their confusion matrices, correct classification rates, robustness to noise, and computational complexity. The effect of the value of N and different signal-to-noise ratios (SNRs) on correct classification performance is investigated for each classifier. Decision-tree classifier is found to be the most suitable classifier because of its high classification rate, robustness to noise, and computational ease. Real data acquired by ASELSAN Inc. is also used to validate the algorithm. The results of the real data indicate that the algorithm is ready for deployment in the field and is capable of being robust against practical complications.en_US
dc.description.statementofresponsibilityEravcı, Bahaeddinen_US
dc.format.extentxi, 65 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectelectronic warfare signal processingen_US
dc.subjectsupport vector machinesen_US
dc.subjectartificial neural networksen_US
dc.subjectdecision treesen_US
dc.subjectnaive Bayes classifiersen_US
dc.subjectpattern recognitionen_US
dc.subjectantenna scan analysisen_US
dc.subjectantenna scanperiod estimationen_US
dc.subjectantenna scan typeen_US
dc.subject.lccTK7871.6 .E73 2010en_US
dc.subject.lcshAntennas (Electronics)en_US
dc.subject.lcshRadar--Antennas.en_US
dc.subject.lcshSignal processing.en_US
dc.subject.lcshPattern recognition systems.en_US
dc.subject.lcshMilitary telecommunication.en_US
dc.titleAutomatic radar antenna scan analysis in electronic warfareen_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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