Near-space hypersonic vehicles are characterized by fast flight speed, good maneuverability and strong surprise capability, which make it a great challenge to predict and intercept such targets. Aiming at the problem of insufficient mining of semantic information and spatial structure information among features in the trajectory prediction process, a trajectory prediction algorithm integrating the multi-attention mechanism and the encoder-decoder structure based on LSTM is proposed. Firstly, the kinematic analysis of the hypersonic vehicle is carried out to construct the vehicle trajectory database under six-dimensional features, and then the encoder and decoder fusing the multi-head attention mechanism and LSTM neural network are designed. On this basis, the time-step data is changed to make the network output the trajectory sequence in the future period. The experimental results show that the model improves 29% and 13% in the two metrics of MAE and MAPE compared with similar methods, and realizes the effective prediction of multiple flight trajectories of hypersonic vehicles.


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    Titel :

    Hypersonic Vehicle Trajectory Prediction Algorithm Incorporating Multiple Attention and Coding-Decoding Structures


    Beteiligte:
    Ding, Zirui (Autor:in) / Zhou, Jie (Autor:in) / Chen, Chaobo (Autor:in) / Gao, Song (Autor:in)


    Erscheinungsdatum :

    17.11.2023


    Format / Umfang :

    718575 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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