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dc.contributor.authorCatak, Ferhat Özgur
dc.contributor.authorImran, Md Abdullah Al
dc.contributor.authorDalveren, Yaser
dc.contributor.authorYildiz, Beytullah
dc.contributor.authorKara, Ali
dc.date.accessioned2024-12-13T13:19:37Z
dc.date.available2024-12-13T13:19:37Z
dc.date.created2024-11-05T18:57:17Z
dc.date.issued2024
dc.identifier.citationCatak, F. O., Al Imran, M. A., Dalveren, Y., Yildiz, B., & Kara, A. (2024). Radar Emitter Localization Based on Multipath Exploitation Using Machine Learning. IEEE Access, 12.en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11250/3169687
dc.description.abstractIn this study, a Machine Learning (ML)-based approach is proposed to enhance the computational efficiency of a particular method that was previously proposed by the authors for passive localization of radar emitters based on multipath exploitation with a single receiver in Electronic Support Measures (ESM) systems. The idea is to utilize a ML model on a dataset consisting of useful features obtained from the priori-known operational environment. To verify the applicability and computational efficiency of the proposed approach, simulations are performed on the pseudo-realistic scenes to create the datasets. Well-known regression ML models are trained and tested on the created datasets. The performance of the proposed approach is then evaluated in terms of localization accuracy and computational speed. Based on the results, it is verified that the proposed approach is computationally efficient and implementable in radar detection applications on the condition that the operational environment is known prior to implementation.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectmaskinlæringen_US
dc.subjectmachine learningen_US
dc.subjectradaren_US
dc.titleRadar Emitter Localization Based on Multipath Exploitation Using Machine Learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2024 The Authors.en_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.source.volume12en_US
dc.source.journalIEEE Accessen_US
dc.identifier.doi10.1109/ACCESS.2024.3488959
dc.identifier.cristin2318004
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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