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Multiple PDAF Algorithm for Estimation States Multiple of the Ships

다중 선박의 상태추정을 위한 Multiple PDAF 알고리즘

  • Jaeha Choi (Department of Naval Architecture and Ocean Engineering, Chungnam National University) ;
  • Jeonghong Park (Advanced-intelligent Ship Research Division, Korea Research Institute of Ship and Ocean Engineering) ;
  • Minju Kang (Advanced-intelligent Ship Research Division, Korea Research Institute of Ship and Ocean Engineering) ;
  • Hyejin Kim (Advanced-intelligent Ship Research Division, Korea Research Institute of Ship and Ocean Engineering) ;
  • Wonkeun Youn (Department of Autonomous Vehicle System Engineering, Chungnam National University)
  • 최재하 (충남대학교 선박해양공학과) ;
  • 박정홍 (한국해양과학기술원 부설 선박해양플랜트연구소 지능형선박연구본부) ;
  • 강민주 (한국해양과학기술원 부설 선박해양플랜트연구소 지능형선박연구본부) ;
  • 김혜진 (한국해양과학기술원 부설 선박해양플랜트연구소 지능형선박연구본부) ;
  • 윤원근 (충남대학교 자율운항시스템공학과)
  • Received : 2023.04.01
  • Accepted : 2023.06.12
  • Published : 2023.08.20

Abstract

In order to implement the autonomous navigation function, it is essential to track an object within a certain radius of the ship's route. This paper proposes the Multiple Probabilistic Data Association Filter (MPDAF), which can track multiple ships by extending Probabilistic Data Association Filter (PDAF), an existing single object tracking algorithm, using radar data obtained from real marine environments. The proposed MPDAF algorithm was developed to address the problem of tracking multiple objects in a complex environment where there can be significant uncertainty in the number and identification of objects to be tracked. Using real-world radar data provided by the German aerospace center (DLR), it has been verified that the proposed algorithm can track a large number of objects with a small position error.

Keywords

Acknowledgement

이 논문은 2023년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원의 지원(P0017006, 2023년 산업혁신인재성장지원사업) 및 해양수산부 재원으로 해양수산과학기술진흥원의 지원(1525014528, "스마트항만-자율운항선박 연계기술 개발")을 받아 수행된 연구임.

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