Hypersonic glide vehicles (HGVs) have the characteristics of high mobility and penetration ability, which make trajectory prediction very complicated and bring great challenges to the defence system. Accurate prediction of the target's flight trajectory helps the defence system make correct interception decisions. In this paper, we propose an HGV trajectory prediction method based on temporal convolutional network (TCN). The input of the network is the six-dimensional characteristics of the aircraft, including the position, speed, ballistic inclination and course angle, and the output is the trajectory for some time in the future. In addition, we compare and analyse the prediction effect of long short-term memory network (LSTM) and temporal convolutional network. The results show that the TCN has better performance than LSTM in long-term prediction.


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    Titel :

    Hypersonic vehicle trajectory prediction based on temporal convolutional network


    Beteiligte:
    Luo, J. (Autor:in) / Cai, Y. (Autor:in) / Jiang, H. (Autor:in)


    Erscheinungsdatum :

    01.01.2023


    Format / Umfang :

    995781 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch