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dc.contributor.authorLee, JH-
dc.contributor.authorChoi, IS-
dc.contributor.authorKim, HT-
dc.date.accessioned2016-03-31T12:42:33Z-
dc.date.available2016-03-31T12:42:33Z-
dc.date.created2009-03-18-
dc.date.issued2003-12-
dc.identifier.issn1053-587X-
dc.identifier.other2003-OAK-0000003841-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/18231-
dc.description.abstractTime domain response-based neural networks and frequency domain response-based neural networks have been proposed for radar target recognition. In this study, we propose a natural frequency-based neural network for radar target recognition. Our scheme takes advantage of an aspect angle independence of a natural frequency. It is shown from experimental results that a natural frequency based-neural network using the first natural frequency pair is superior to a time domain response-based neural network in the case of a single aspect angle and that a natural frequency based-neural network using the first natural frequency pair or the first two natural frequency pairs is superior to a time domain response-based neural network in the case of a multiple aspect angle.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGI-
dc.relation.isPartOfIEEE TRANSACTIONS ON SIGNAL PROCESSING-
dc.subjectexponentially damped sinusoids-
dc.subjectfrequency domain response-
dc.subjectnatural frequency-
dc.subjectneural networks-
dc.subjectparameter estimation-
dc.subjectradar target recognition-
dc.subjecttime domain response-
dc.subjectEXPONENTIALLY DAMPED SINUSOIDS-
dc.subjectHIGHER-ORDER STATISTICS-
dc.subjectPARAMETER-ESTIMATION-
dc.subjectMATRIX PENCIL-
dc.subjectNOISE-
dc.titleNatural frequency-based neural network approach to radar target recognition-
dc.typeArticle-
dc.contributor.college전자전기공학과-
dc.identifier.doi10.1109/TSP.2003.818-
dc.author.googleLee, JH-
dc.author.googleChoi, IS-
dc.author.googleKim, HT-
dc.relation.volume51-
dc.relation.issue12-
dc.relation.startpage3191-
dc.relation.lastpage3197-
dc.contributor.id10051137-
dc.relation.journalIEEE TRANSACTIONS ON SIGNAL PROCESSING-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationIEEE TRANSACTIONS ON SIGNAL PROCESSING, v.51, no.12, pp.3191 - 3197-
dc.identifier.wosid000186687300020-
dc.date.tcdate2019-01-01-
dc.citation.endPage3197-
dc.citation.number12-
dc.citation.startPage3191-
dc.citation.titleIEEE TRANSACTIONS ON SIGNAL PROCESSING-
dc.citation.volume51-
dc.contributor.affiliatedAuthorKim, HT-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc15-
dc.type.docTypeArticle-
dc.subject.keywordAuthorexponentially damped sinusoids-
dc.subject.keywordAuthorfrequency domain response-
dc.subject.keywordAuthornatural frequency-
dc.subject.keywordAuthorneural networks-
dc.subject.keywordAuthorparameter estimation-
dc.subject.keywordAuthorradar target recognition-
dc.subject.keywordAuthortime domain response-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-

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