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dc.contributor.advisorArıkan, Orhan
dc.contributor.authorGüldoğan, Mehmet Burak
dc.date.accessioned2016-01-08T18:16:45Z
dc.date.available2016-01-08T18:16:45Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/11693/15321
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.en_US
dc.descriptionThesis (Ph. D.) -- Bilkent University, 2010.en_US
dc.descriptionIncludes bibliographical references leaves 144-158.en_US
dc.description.abstractIn this thesis, novel array signal processing techniques are proposed for identifi- cation of multipath communication channels based on cross ambiguity function (CAF) calculation, swarm intelligence and compressed sensing (CS) theory. First technique detects the presence of multipath components by integrating CAFs of each antenna output in the array and iteratively estimates direction-of-arrivals (DOAs), time delays and Doppler shifts of a known waveform. Second technique called particle swarm optimization-cross ambiguity function (PSO-CAF) makes use of the CAF calculation to transform the received antenna array outputs to delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components are identified by using a simple amplitude thresholding in the delay-Doppler domain. PSO is used to estimate parameters of the multipath components in each cluster. Third proposed technique combines CS theory, swarm intelligence and CAF computation. Performance of standard CS formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this “off-grid”problem, a novel technique by making use of the PSO, that can also be used in applications other than the multipath channel identification is proposed. Performances of the proposed techniques are verified both on sythetic and real data.en_US
dc.description.statementofresponsibilityGüldoğan, Mehmet Buraken_US
dc.format.extentxxi, 158 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectParameter estimationen_US
dc.subjectcross ambiguity function (CAF)en_US
dc.subjectparticle swarm optimization (PSO)en_US
dc.subjectcompressed sensing (CS)en_US
dc.subjectsparse approximationen_US
dc.subject.lccTK5102.9 .G85 2010en_US
dc.subject.lcshSignal processing.en_US
dc.subject.lcshSignal theory (Telecommunication)en_US
dc.subject.lcshEstimation theory.en_US
dc.subject.lcshSwarm intelligence.en_US
dc.subject.lcshParameter estimation.en_US
dc.titleDevelopment of new array signal processing techniques using swarm intelligenceen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US


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