引入虚拟目标的高超声速巡航导弹智能机动突防策略  

Intelligent Penetration Policy for Hypersonic Cruise Missiles Based on Virtual Targets

作　　者：李加申 王晓芳[1] 林海[1] LI Jiashen;WANG Xiaofang;LIN Hai(School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京理工大学宇航学院,北京100081

出　　处：《兵工学报》2024年第11期3856-3867,共12页Acta Armamentarii

基　　金：国家自然科学基金项目(11502019)。

摘　　要：针对高超声速巡航导弹机动突防时弹道偏离难以约束、突防策略对不同作战场景的泛化性能较差等问题,提出一种基于虚拟目标和上下文马尔可夫决策过程的智能机动突防决策算法。在以预定弹道为轴线的管状弹道包络面内选定多个静止的虚拟目标,采用深度强化学习算法对其相对预定弹道的位置参数进行决策;用比例导引律引导巡航弹依次攻击这些虚拟目标,在包络面内塑造出能满足突防要求的机动弹道。基于上下文马尔可夫决策过程,将针对单个作战场景的最优突防策略拓展到作战场景的概率分布上,提升突防策略对不同作战场景的适应性。仿真结果表明:该智能机动突防策略能在突防的同时约束弹道偏离,在拦截弹发射位置和机动能力发生变化时仍能保持良好性能。An intelligent penetration policy using virtual targets and contextual Markov decision process(CMDP)for hypersonic cruise missiles is proposed to constrain the trajectory deviation and improve the generalization performance in different combat scenarios.The stationary virtual targets are chosen within a tubular envelope with the planned trajectory as axis,and the deep reinforcement learning algorithm is applied to decide their position relative to the axis.Then the proportional guidance law is used to guide the cruise missile to attack these virtual targets one by one with proportional guidance law,thus shaping a maneuvering trajectory meeting the requirements of penetration within the given envelope.The optimal penetration policy for a combat scenario is extended to the probability distribution of combat scenarios using CMDP to improve the generalization performance.The results demonstrate that the penetration policy constrains the trajectory deviation during penetraton and exhibits adaptability to variations of interceptor's launch position and maneuvering capability.

关 键 词：高超声速巡航导弹 机动突防 虚拟目标 上下文马尔可夫决策 强化学习 

分 类 号：V249.31[航空宇航科学与技术—飞行器设计]