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Abstract For the safe operation of a ship, it is necessary to detect and track nearby ships, called target ships, accurately. To track the target ships, Kalman filters (KF) have been widely used. However, when the system model of the KF, which predicts the motion of the system, may be different from the actual motion of the target ships, the accuracy could decrease. To solve this problem, the Interactive Multi-Model (IMM) method can be adopted, which follows several system models at the same time, but the number of system models that the IMM implements is limited. Therefore, a method that can analyze the actual dynamic behavior of the target ships and track accurately the target ships based on historical data is required. Recently, deep learning has been applied to tracking methods to solve existing problems and improve accuracy. For tracking, the Long-Short Term Memory (LSTM) method is being mainly applied and shows better performance in the marine environment where the motion characteristics of the target ships could change.
In this study, a tracking method based on deep learning is proposed to track nearby target ships. First, we construct a tracking filter using the LSTM-KF method, which combines LSTM and traditional KF. In the LSTM-KF, the system model and system noise of the KF are trained by previous data and predicted using deep learning. There is also a method of constructing an LSTM model that directly produces the track of the target ships as output data. After constructing the tracking filter using LSTM, the tracking filter is trained using the ship’s navigation data. In this study, the proposed method is compared with the tracking results using the traditional KF, and it was confirmed that it works effectively.
Publication Date 2022-11-06
Yeongmin Jo, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "A Ship Tracking Method under Dynamic Characteristic Changes with LSTM", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10

List of Articles
번호 분류 제목 Publication Date
56 Domestic Conference 공민철, 노명일, 이혜원, 전도현, 조영민, 박정호, "자율 운항 기술 검증을 위한 VR 기반 시뮬레이션 프로그램", 2023년도 한국CDE학회 동계학술발표회, 평창, pp. 48, 2023.02.08-11 file 2023-02-09
55 Domestic Conference 송하민, 노명일, 하지상, 김기수, "해군 함정의 승조원 구성 시뮬레이션 방법", 2022년도 대한조선학회 추계학술발표회, 창원, 2022.11.09-11 2022-11-10
54 Domestic Conference 전도현, 노명일, 이혜원, 조영민, 진은석, 유동훈 "선박 정보의 불확실성을 고려한 확률론적 충돌 위험도 산정", 2021년도 한국CDE학회 하계학술발표회, 제주, pp. 94, 2021.08.25-28 file 2021-08-26
53 International Conference Ki-Su Kim, Myung-Il Roh, Seung-Ho Ham, Sol Ha, "Evacuation Analysis of Passenger Ships Considering Intermediate Flooding", Proceedings of International Symposium on PRADS 2022, Dubrovnik, Croatia, pp. 628-632, 2022.10.09-13 file 2022-10-10
52 International Conference Myung-Il Roh, "Simulation Based Engineering for Ship and Offshore Plant", International Ocean Technology Conference & Expo (IOTCE 2015), Qingdao, China, 2015.09.01-03 file 2015-09-02
51 International Conference Myung-Il Roh, "Physics-based Simulation for Design, Production, and Installation of Ships and Offshore Structures", International Symposium on Computational Design and Engineering, Ho Chi Minh, Vietnam, 2017.12.13-16 2017-12-15
50 Domestic Conference 노명일, "선박 설계, 생산 및 운용 단계에서의 딥 러닝 활용 예, 2020년도 선박해양플랜트구조연구회 워크샵, 2021.02.18 2021-02-18
49 International Conference Myung-Il Roh, "Applications of Deep Learning in Ship Design, Production, and Operation Stages", Proceedings of ICDM(International Conference on Decarbonization and Digitalization in Marine Engineering) 2022, Siheung, Korea, 2022.04.28-29 2022-04-29
48 International Conference Min-Chul Kong, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "A Detection Method of Objects with Text in Drawings Based on Deep Learning", Proceedings of ISOPE 2023, Ottawa, Canada, 2023.06.19-23 file 2023-06-22
47 Domestic Conference 여인창, 노명일, 이혜원, 유동훈, "선박용 서라운드 뷰 영상의 자동 생성 방법", 2023년도 한국CDE학회 동계학술발표회, 평창, pp. 45, 2023.02.08-11 file 2023-02-09
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