Radar Emitter Localization Based on Multipath Exploitation Using Machine Learning

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dc.authoridKara, Ali/0000-0002-9739-7619
dc.authoridDalveren, Yaser/0000-0002-9459-0042
dc.authoridCatak, Ferhat Ozgur/0000-0002-2434-9966
dc.authoridYILDIZ, Beytullah/0000-0001-7664-5145
dc.contributor.authorCatak, Ferhat Ozgur
dc.contributor.authorAl Imran, Md Abdullah
dc.contributor.authorDalveren, Yaser
dc.contributor.authorYildiz, Beytullah
dc.contributor.authorKara, Ali
dc.date.accessioned2025-03-20T09:50:48Z
dc.date.available2025-03-20T09:50:48Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
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.
dc.identifier.doi10.1109/ACCESS.2024.3488959
dc.identifier.endpage163381
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85208407156
dc.identifier.scopusqualityQ1
dc.identifier.startpage163367
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3488959
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2325
dc.identifier.volume12
dc.identifier.wosWOS:001358495400026
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectESM
dc.subjectGDOP
dc.subjectlocalization
dc.subjectmachine learning
dc.subjectmultipath exploitation
dc.subjectradar
dc.subjectTDOA
dc.subjectESM
dc.subjectGDOP
dc.subjectlocalization
dc.subjectmachine learning
dc.subjectmultipath exploitation
dc.subjectradar
dc.subjectTDOA
dc.titleRadar Emitter Localization Based on Multipath Exploitation Using Machine Learning
dc.typeArticle

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