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    基于强化学习的高射炮协同防空作战技术

    Cooperative Air Defense Operations Technology Based on Reinforcement Learning

    • 摘要: 面向临地安防体系下低空安防智能化作战的迫切需求,提出了一种针对低空无人机的高射炮协同防空作战博弈框架,并建立了攻防双方的仿真模型。针对临地安防场景下低空无人机拦截难题,提出了基于多智能体强化学习的“炮−机”博弈对抗进化策略框架,通过设计引导性奖励函数解决了奖励稀疏性的困境。仿真实验结果表明,高射炮能够高效拦截不断改变进攻策略的无人机,为未来临地安防体系下低空防空的智能化发展提供了强有力的技术支撑。

       

      Abstract: In response to the urgent demand for intercepting low-altitude unmanned aerial vehicles (UAVs) in vicineland security system, a game framework is proposed for the coordinated air defense operations of anti-aircraft (AA) artillery against unmanned air vehicles in low altitude, and the simulation model of the offensive and defensive sides is built. As for the interception problems of low altitude UAV in vicinagearth security scenario, a game confrontation evolution strategy framework of "artillery-plane" is proposed for the first time based on multi-agent reinforcement learning. By designing guiding reward functions, the difficulty of reward sparsity is addressed. The simulation results demonstrate the high effectiveness of UADA in intercepting UAVs with evolving attack strategies. This provides robust technical support for the intelligent development of low-altitude air defense in future vicineland security scenarios.

       

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