Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9016
Full metadata record
DC FieldValueLanguage
dc.contributor.authorOnur Y.-
dc.contributor.authorHayvaci H.T.-
dc.date.accessioned2022-11-30T19:26:29Z-
dc.date.available2022-11-30T19:26:29Z-
dc.date.issued2022-
dc.identifier.isbn9.78167E+12-
dc.identifier.urihttps://doi.org/10.1109/MetroAeroSpace54187.2022.9855981-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/9016-
dc.description9th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2022 -- 27 June 2022 through 29 June 2022 -- -- 182210en_US
dc.description.abstractIn this study, we propose a new method with an artificial intelligence infrastructure to estimate polarization of a radar signal. In modern electronic warfare and radar systems polarization of the return signal now poses essential information. Subspace-based algorithms such as MUSIC and ESPRIT have high computational costs for estimating polarization. Computational cost and performance analysis of the proposed method is conducted via simulations and results are discussed along with the existing solutions such as MUSIC algorithm. Simulation results show that the proposed algorithm reduce the computational cost compared to classical MUSIC. The proposed algorithm also reduce the polarization estimation error in low SNR scenarios. © 2022 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 IEEE 9th International Workshop on Metrology for AeroSpace, MetroAeroSpace 2022 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectCost benefit analysisen_US
dc.subjectElectronic warfareen_US
dc.subjectMilitary applicationsen_US
dc.subjectMultiple signal classificationen_US
dc.subjectComputational costsen_US
dc.subjectComputational performanceen_US
dc.subjectConvolutional neural networken_US
dc.subjectCost analysisen_US
dc.subjectEstimation methodsen_US
dc.subjectPerformances analysisen_US
dc.subjectPolarization estimationsen_US
dc.subjectRadar signalsen_US
dc.subjectReturn signalsen_US
dc.subjectSubspace-based algorithmsen_US
dc.subjectPolarizationen_US
dc.titleA Fast Polarization Estimation Method with Convolutional Neural Networksen_US
dc.typeConference Objecten_US
dc.identifier.startpage242en_US
dc.identifier.endpage247en_US
dc.identifier.wosWOS:000861142800045en_US
dc.identifier.scopus2-s2.0-85138009258en_US
dc.identifier.doi10.1109/MetroAeroSpace54187.2022.9855981-
dc.authorscopusid57889639500-
dc.authorscopusid22985309100-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.ozel2022v3_Editen_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:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

Page view(s)

24
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.