Radar Target Recognition Based on Machine Learning

DC FieldValueLanguage
dc.contributor.authorMotyka, Volodymyr
dc.contributor.authorNasalska, Mariia
dc.contributor.authorStepaniak, Yaroslav
dc.contributor.authorVysotska, Victoria
dc.contributor.authorBublyk, Myroslava
dc.contributor.editorHovorushchenko, T.
dc.contributor.editorSavenko, O.
dc.contributor.editorPopov, P.T.
dc.contributor.editorLysenko, S.
dc.date.accessioned2023-07-12T06:59:27Z-
dc.date.available2023-07-12T06:59:27Z-
dc.date.issued2023
dc.identifier.issn1613-0073
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/72081-
dc.descriptionCited by: 0; Conference name: 4th International Workshop on Intelligent Information Technologies and Systems of Information Security, IntellTSIS 2023; Conference date: 22 March 2023 through 24 March 2023; Conference code: 187915
dc.description.abstractThe intelligence of the air situation is based on radar information about the air enemy, which allows you to reveal the raid's target, determine the composition and means that take part in the raid, determine the most dangerous means and ensure that weapons are aimed at them. The article's main goal is to build a target recognition model in the form of AGM-86C (CALCM) and CR Taurus KEPD 350 cruise missiles. A module for recognising AGM-86C and Taurus KEPD 350 cruise missiles have also been built, which sufficiently accurately recognises these types of missiles (accuracy - 84.52%). Because one of the missiles is sometimes referred to as a missile developed using "stealth" technology, the model can be considered effective. The disadvantage of the model is that the model is trained on only two types of missiles. The problem is that the data on the effective scattering surface of all missiles in any state's arsenal is top-secret data. © 2023 Copyright for this paper by its authors.
dc.language.isoen
dc.publisherCEUR-WS
dc.relation.ispartofCEUR Workshop Proceedings
dc.subjectclassification task
dc.subjectClassification tasks
dc.subjectCruise missile
dc.subjectdecision tree
dc.subjectDecision trees
dc.subjectmachine learning
dc.subjectMachine-learning
dc.subjectmissile
dc.subjectMissiles
dc.subjectmodel training
dc.subjectOn-machines
dc.subjectradar
dc.subjectRadar information
dc.subjectRadar target recognition
dc.subjectRecognition models
dc.subjectScattering surface
dc.subjectStealth technology
dc.subjectTarget recognition
dc.subjectTaurus KEPD 350
dc.subjectTraining aircraft
dc.titleRadar Target Recognition Based on Machine Learning
dc.typeconference paper
dc.identifier.scopus2-s2.0-85154036260
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85154036260&partnerID=40&md5=16569c5a6668cc0d488352c5719aaf3d
dc.description.volume3373
dc.description.startpage117 – 128
dcterms.isPartOf.abbreviationCEUR Workshop Proc.
local.import.remainsaffiliations : Lviv Polytechnic National University, S. Bandera Street, 12, Lviv, 79013, Ukraine; Osnabrück University, Friedrich-Janssen-Str. 1, Osnabrück, 49076, Germany
local.import.remainspublication_stage : Final
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