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인공지능 기반 유사한 형상을 가진 표적 식별 가능성 확인을 위한 레이더 데이터 분석

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dc.contributor.author김아란-
dc.contributor.author김하선-
dc.contributor.author강창호-
dc.contributor.author김선영-
dc.date.accessioned2022-05-13T06:40:04Z-
dc.date.available2022-05-13T06:40:04Z-
dc.date.created2022-04-25-
dc.date.issued2022-04-
dc.identifier.issn1976-5622-
dc.identifier.urihttps://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21046-
dc.description.abstractIn this work, we analyzed radar data to check the feasibility of identifying targets with similar shapes based on artificial intelligence. Among radar measurements, radar cross section(RCS) and high-resolution range profile(HRRP) were selected and used as the classification metrics. Before performing artificial intelligence learning, the structural similarity index measure was selected as the performance index and used to verify the feasibility of target classification. We modeled various targets with similar shapes and then obtained radar data using Ansys HFSS. From similar test results, we confirmed that targets with similar shapes could be identified and the possibility of classification in the case of HRRP is higher than that in the case of RCS.-
dc.language한국어-
dc.language.isoko-
dc.publisher제어·로봇·시스템학회-
dc.title인공지능 기반 유사한 형상을 가진 표적 식별 가능성 확인을 위한 레이더 데이터 분석-
dc.title.alternativeRadar Data Analysis for Feasibility Study on Identifying Targets with Similar Shapes Based on Artificial Intelligence-
dc.typeArticle-
dc.contributor.affiliatedAuthor강창호-
dc.identifier.doi10.5302/J.ICROS.2022.22.8002-
dc.identifier.bibliographicCitation제어.로봇.시스템학회 논문지, v.28, no.4, pp.391 - 396-
dc.relation.isPartOf제어.로봇.시스템학회 논문지-
dc.citation.title제어.로봇.시스템학회 논문지-
dc.citation.volume28-
dc.citation.number4-
dc.citation.startPage391-
dc.citation.endPage396-
dc.type.rimsART-
dc.identifier.kciidART002827611-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorradar cross section-
dc.subject.keywordAuthorhigh-resolution range profile-
dc.subject.keywordAuthorstructural similarity index measure-
dc.subject.keywordAuthoridentification-
dc.subject.keywordAuthor.-
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