Novel methods for SAR imaging problems

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buir.advisorArıkan, Orhan
dc.contributor.authorUğur, Salih
dc.date.accessioned2016-01-08T18:26:10Z
dc.date.available2016-01-08T18:26:10Z
dc.date.issued2013
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.en_US
dc.descriptionThesis (Ph. D.) -- Bilkent University, 2013.en_US
dc.descriptionIncludes bibliographical references leaves 62-70.en_US
dc.description.abstractSynthetic Aperture Radar (SAR) provides high resolution images of terrain reflectivity. SAR systems are indispensable in many remote sensing applications. High resolution imaging of terrain requires precise position information of the radar platform on its flight path. In target detection and identification applications, imaging of sparse reflectivity scenes is a requirement. In this thesis, novel SAR image reconstruction techniques for sparse target scenes are developed. These techniques differ from earlier approaches in their ability of simultaneous image reconstruction and motion compensation. It is shown that if the residual phase error after INS/GPS corrected platform motion is captured in the signal model, then the optimal autofocused image formation can be formulated as a sparse reconstruction problem. In the first proposed technique, Non-Linear Conjugate Gradient Descent algorithm is used to obtain the optimum reconstruction. To increase robustness in the reconstruction, Total Variation penalty is introduced into the cost function of the optimization. To reduce the rate of A/D conversion and memory requirements, a specific under sampling pattern is introduced. In the second proposed technique, Expectation Maximization Based Matching Pursuit (EMMP) algorithm is utilized to obtain the optimum sparse SAR reconstruction. EMMP algorithm is greedy and computationally less complex resulting in fast SAR image reconstructions. Based on a variety of metrics, performances of the proposed techniques are compared. It is observed that the EMMP algorithm has an additional advantage of reconstructing off-grid targets by perturbing on-grid basis vectors on a finer grid.en_US
dc.description.degreePh.D.en_US
dc.description.statementofresponsibilityUğur, Salihen_US
dc.format.extentxiii, 73 leaves, tables, graphsen_US
dc.identifier.urihttp://hdl.handle.net/11693/15887
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSynthetic Aperture Radaren_US
dc.subjectPhase Error Correctionen_US
dc.subjectCompressed Sensingen_US
dc.subjectTotal Variationen_US
dc.subjectExpectation Maximization Based Matching Pursuiten_US
dc.subject.lccTK6592.S95 U48 2013en_US
dc.subject.lcshSynthetic aperture radar.en_US
dc.subject.lcshImage processing--Mathematics.en_US
dc.subject.lcshSignal processing--Digital techniques.en_US
dc.titleNovel methods for SAR imaging problemsen_US
dc.typeThesisen_US

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