Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6274
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dc.contributor.authorÇamlıca, Sedat-
dc.contributor.authorGüven, H. Emre-
dc.contributor.authorGürbüz, Ali Cafer-
dc.contributor.authorArıkan, Orhan-
dc.date.accessioned2021-09-11T15:35:34Z-
dc.date.available2021-09-11T15:35:34Z-
dc.date.issued2015en_US
dc.identifier.citation3rd International Workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa) -- JUN 17-19, 2015 -- Pisa, ITALYen_US
dc.identifier.isbn978-1-4799-7420-7-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6274-
dc.description.abstractSynthetic Aperture Radar (SAR) has significance in many remote sensing applications. One of the main problems with SAR is the platform motion that causes defocusing in the reconstructed SAR image. To mitigate this problem, for particularly on imaging of fields that admit a sparse representation, various sparsity based techniques that either apply optimization procedures or greedy iterative solutions have been proposed in the literature. Although these techniques have been mainly compared with classical phase gradient autofocus (PGA) algorithm, they have not been analyzed and compared with each other. In this paper several of the recent sparsity based SAR phase correction techniques are compared using metrics such as mean square error (MSE), entropy, target to background ratio (TBR) in terms of undersampling ratio, signal to noise ratio (SNR). In addition to comparisons, a cross validation based stopping criterion is introduced with an OMP procedure to free the algorithm from user defined parameters. The techniques are tested on simulated data for detailed comparisons. Real data results of tested techniques are also provided. Our initial results show that all compared sparsity based techniques provide better performance compared to PGA with varying relative performances.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2015 3Rd International Workshop On Compressed Sensing Theory And Its Application To Radar, Sonar, And Remote Sensing (Cosera)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleAnalysis of Sparsity Based Joint SAR Image Reconstruction and Autofocus Techniquesen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.startpage99en_US
dc.identifier.endpage103en_US
dc.authorid0000-0001-8923-0299-
dc.authorid0000-0002-3698-8888-
dc.identifier.wosWOS:000381629200021en_US
dc.identifier.scopus2-s2.0-84962833068en_US
dc.institutionauthorGürbüz, Ali Cafer-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference3rd International Workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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