In this paper, the real-time cooperative guidance strategy for the aircraft trying to evade an incoming attacking missile was developed. The aircraft performs evasive maneuvers, launching a defender missile to divert the missile. Instead of classical strategies, which are based on optimal control or differential game, the engagement was formulated as a multi-agent game. The problem was solved by using the deep reinforcement learning method. To address the sparse reward problem, a general reward design method was presented utilizing the shaping technique. Guidance law, reward function, and training approach were demonstrated through the learning process and simulations. The application of the non-sparse reward function accelerated the model convergence. Considering a standard optimal guidance law as a benchmark, the effectiveness and advantages (guarantees of the aircraft's escape and win rates in multi-agent game) of the proposed guidance strategy were validated by the simulation results. Compared to the standard optimal guidance law, the proposed guidance strategy performed better with less prior knowledge.


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    Titel :

    Real-time Guidance Strategy for Active Defense Aircraft via Deep Reinforcement Learning


    Beteiligte:
    Li, Zhi (Autor:in) / Wu, Jinze (Autor:in) / Wu, Yuanpei (Autor:in) / Zheng, Yu (Autor:in) / Li, Meng (Autor:in) / Liang, Haizhao (Autor:in)


    Erscheinungsdatum :

    16.08.2021


    Format / Umfang :

    1231536 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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