Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6346
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dc.contributor.authorGürbüz, Ali Cafer-
dc.contributor.authorCevher, Volkan-
dc.contributor.authorMcClellan, James H.-
dc.date.accessioned2021-09-11T15:35:57Z-
dc.date.available2021-09-11T15:35:57Z-
dc.date.issued2012en_US
dc.identifier.issn0018-9251-
dc.identifier.issn1557-9603-
dc.identifier.urihttps://doi.org/10.1109/TAES.2012.6178067-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6346-
dc.description.abstractBearing estimation algorithms obtain only a small number of direction of arrivals (DOAs) within the entire angle domain, when the sources are spatially sparse. Hence, we propose a method to specifically exploit this spatial sparsity property. The method uses a very small number of measurements in the form of random projections of the sensor data along with one full waveform recording at one of the sensors. A basis pursuit strategy is used to formulate the problem by representing the measurements in an overcomplete dictionary. Sparsity is enforced by l(1)-norm minimization which leads to a convex optimization problem that can be efficiently solved with a linear program. This formulation is very effective for decreasing communication loads in multi sensor systems. The algorithm provides increased bearing resolution and is applicable for both narrowband and wideband signals. Sensors positions must be known, but the array shape can be arbitrary. Simulations and field data results are provided to demonstrate the performance and advantages of the proposed method.en_US
dc.description.sponsorshipMarie Curie IRGEuropean Commission [PIRG04-GA-2008-239506]; AROMURIMURI [DAAD19-02-1-0252]en_US
dc.description.sponsorshipThis work was supported by the Marie Curie IRG Grant "Compressive Data Acquisition and Processing Techniques for Sensing Applications" with Grant PIRG04-GA-2008-239506, and an AROMURI Grant: "Multi-Modal Inverse Scattering for Detection and Classification of General Concealed Targets," Contract DAAD19-02-1-0252.en_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Transactions On Aerospace And Electronic Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleBearing Estimation Via Spatial Sparsity Using Compressive Sensingen_US
dc.typeArticleen_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.volume48en_US
dc.identifier.issue2en_US
dc.identifier.startpage1358en_US
dc.identifier.endpage1369en_US
dc.authorid0000-0001-8923-0299-
dc.authorid0000-0001-8923-0299-
dc.identifier.wosWOS:000302647400029en_US
dc.identifier.scopus2-s2.0-84859855594en_US
dc.institutionauthorGürbüz, Ali Cafer-
dc.identifier.doi10.1109/TAES.2012.6178067-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
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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