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dc.contributor.author전상운-
dc.date.accessioned2023-05-17T05:16:09Z-
dc.date.available2023-05-17T05:16:09Z-
dc.date.issued2018-08-
dc.identifier.citation한국통신학회논문지, v. 43, NO. 8, Page. 1382.0-1385.0-
dc.identifier.issn1226-4717;2287-3880-
dc.identifier.urihttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07528660en_US
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/180692-
dc.description.abstract본 논문은 기계학습 기반의 레이다 기술의 연구 동향을 요약한다. 특히, 본 논문에서는 레이다 시스템, 레이다 안테나, 레이다 송수신장치, 레이다 신호처리기등 각 분야에 적용된 기계학습기술에 관하여 분석하고연구의 발전 방향에 관하여 논한다.;In this letter, we summarize recent research trends of machine learning (ML) techniques for radar applications. In particular, we first categorize the radar technique components as follows: Radar system, radar antenna, radar transmitter & receiver, and radar signal processor. Then, we investigate the future research directions of recent proposed ML-based radar techniques.-
dc.description.sponsorship이 연구는 국방과학연구소 (ADD)의 지원을 받았습니다.-
dc.languageko-
dc.publisher한국통신학회-
dc.subjectCognitive Radar-
dc.subjectmachine learning-
dc.subjectdeep learning-
dc.subjectradar resource management-
dc.subjectradar signal processing-
dc.title기계학습기반 레이다기술 최신연구 동향-
dc.title.alternativeRecent Research Trends of Machine Learning-Based Radar Techniques-
dc.typeArticle-
dc.relation.no8-
dc.relation.volume43-
dc.identifier.doi10.7840/kics.2018.43.8.1382-
dc.relation.page1382.0-1385.0-
dc.relation.journal한국통신학회논문지-
dc.contributor.googleauthor김동환-
dc.contributor.googleauthor정방철-
dc.contributor.googleauthor김영철-
dc.contributor.googleauthor전상운-
dc.contributor.googleauthor김진웅-
dc.sector.campusE-
dc.sector.daehak공학대학-
dc.sector.department국방정보공학과-
dc.identifier.pidsangwoonjeon-
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MILITARY INFORMATION ENGINEERING(국방정보공학과) > Articles
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