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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

  1. No Image 14Feb
    by SyDLab
    in International Conference

    Do-Hyeok Ahn, Myung-Il Roh, In-Chang Yeo, Do-Hyun Chun, "A Method for Automatic Control of the Block Lifting by an Offshore Floating Crane Based on Deep Reinforcement Learning", Proceedings of ISOPE 2025, Goyang, Korea, 2025.06.01-06

  2. No Image 02May
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    Min-Chul Kong, Myung-Il Roh, In-Su Han, Mijin Kim, Jeoungyoun Kim, "A Method for Pipe Auto-routing Using Graph and Octree Structure", Proceedings of G-NAOE 2024, Southampton, UK, 2024.11.05-09

  3. No Image 01May
    by SyDLab
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    Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, "A Method for the Automatic Generation of Hull Form Surfaces Based on MLP (Multi-Layer Perceptron) Considering Design Requirements", Proceedings of G-NAOE 2024, Southampton, UK, 2024.11.05-09

  4. No Image 01May
    by SyDLab
    in International Conference

    In-Su Han, Myung-Il Roh, Min-Chul Kong, Jeongyoul Lee, Seoyoon Park, "A Search Method for Ship Regulations Considering Document Features", Proceedings of ICCAS 2024, Genoa, Italy, 2024.09.10-12

  5. No Image 01May
    by SyDLab
    in International Conference

    Ha-Yun Kim, Myung-Il Roh, Jisang Ha, In-Chang Yeo, Nam-Sun Son, "A Learning-based Method for Tracking Maritime Obstacles in Real-time", Proceedings of ISOPE 2024, Rhodos, Greece, 2024.06.16-21

  6. No Image 01May
    by SyDLab
    in International Conference

    In-Chang Yeo, Myung-Il Roh, Min-Chul Kong, Dongki Min, Dongguen Jeong, "An Automated Method for the Review of a Ship’s Safety Plan Based on Deep Learning", Proceedings of ISOPE 2024, Rhodos, Greece, 2024.06.16-21

  7. No Image 28Feb
    by SyDLab
    in International Conference

    Jisang Ha, Myung-Il Roh, Min-Chul Kong, Mijin Kim, Jeoungyoun Kim, Nam-Kug Ku, "An Automated Method for Pipe Routing in Ship Unit Modules", Proceedings of IMDC 2024, Amsterdam, Netherland, 2024.06.02-06

  8. No Image 28Feb
    by SyDLab
    in International Conference

    Min-Chul Kong, Myung-Il Roh, In-Chang Yeo, In-Su Han, Dongki Min, Dongguen Jeong, "Methods for Graph Conversion and Pattern Recognition for P&IDs", Proceedings of IMDC 2024, Amsterdam, Netherland, 2024.06.02-06

  9. No Image 23Oct
    by SyDLab
    in International Conference

    Yeong-min Jo, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Do-Hyun Chun, Min-chul Kong, "An Improved Method for the Sensor Fusion for Autonomous Ships", Proceedings of PAAMES/AMEC 2023, Kyoto, Japan, 2023.10.18-20

  10. No Image 22Jan
    by SyDLab
    in International Conference

    In-Chang Yeo, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "A Method for Automatic Berthing of a Ship Using LIDAR", Proceedings of PAAMES/AMEC 2023, Kyoto, Japan, 2023.10.18-20

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