The trajectory prediction of maritime targets is an urgent problem to be solved in order to effectively manage and control the situation in the sea area. The UAV has the advantages of strong real-time performance, high resolution and relatively stable viewing angle. Trajectory prediction of targets at sea. In this paper, a method of ship track prediction based on sliding window based bidirectional long short-term memory network (Bi-LSTM) is proposed using ship track data. Considering the high feature dimension of ship position information, the LSTM network theory is used to construct a ship track prediction model based on the sliding window BLSTM network, and the spatiotemporal features of the ship are extracted, and the position, speed and heading output of the network are used to predict the ship track. The simulation results show that, compared with the prediction results of LSTM and RNN, the prediction accuracy and generalization ability of the sliding window BLSTM network are better in the direct flight, steering and maneuvering scenarios.


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    Titel :

    Ship Track Prediction Based on Sliding Window BLSTM Network


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Fu, Wenxing (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Hu, Weijun (Autor:in) / Zhang, Weijie (Autor:in) / Xiong, Jingyi (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2022 ; Xi'an, China September 23, 2022 - September 25, 2022



    Erscheinungsdatum :

    10.03.2023


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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