This paper proposes a ballistic missile maneuver strategy based on proximal policy optimization (PPO) reinforcement learning algorithm, which enables the ballistic missile to evade the interceptor in the midcourse. Firstly, the extra-atmospheric engagement process was modeled into a Markov Decision Process. The thrust of the ballistic missile is regarded as the action of the agent, and the energy consumption is regarded as the reward of the agent. Importantly, the observations only consist of the seeker angle and their rate of change with no range, velocity, and acceleration estimation, making it possible to applying to the passive seekers outside the atmosphere. Simulation shows that the ballistic missile agent based on PPO algorithm could evade the interceptor with a 100% success rate. Compared with traditional differential game methods, reinforcement learning-based methods could map the observations to the divert thruster command, which greatly reduces the time of real-time computation.


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    Titel :

    Ballistic Missile Midcourse Intelligent Maneuver Strategy Based on PPO Algorithm


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Deng, Yimin (Herausgeber:in) / Yan, Mengda (Autor:in) / Yang, Rennong (Autor:in) / Zhao, Xiaoru (Autor:in) / Yue, Longfei (Autor:in)

    Kongress:

    International Conference on Guidance, Navigation and Control ; 2022 ; Harbin, China August 05, 2022 - August 07, 2022



    Erscheinungsdatum :

    2023-01-31


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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