信息非完备下飞行器智能主动防御制导方法  

Intelligent Active Defense Guidance for Hypersonic Vehicle with Incomplete Information

作　　者：倪炜霖 丘沛桓 柳明军 曾景岚 梁海朝 NI Weilin;QIU Peihuan;LIU Mingjun;ZENG Jinglan;LIANG Haizhao(School of Aeronautics and Astronautics,Sun Yat-sen University,Shenzhen 518107)

机构地区：[1]中山大学航空航天学院,深圳518107

出　　处：《飞控与探测》2025年第1期47-56,共10页Flight Control & Detection

基　　金：国家自然科学基金(62003375,62103452)。

摘　　要：研究了在“目标拦截者防御者”这类多角色博弈对抗场景中的高超声速飞行器智能主动防御制导方法。针对携带主动防御系统的高超声速飞行器在攻防对抗过程中面临的观测信息非完备问题,如缺失探测信息及存在测量噪声等,提出一种基于卷积深度Q网络的智能主动防御制导方法,以实现飞行器在信息非完备条件下的有效博弈对抗。首先,基于飞行器运动状态的时空连续性,构建一种信息堆叠机制,形成在时间维度扩展的非完备观测信息组;然后,利用所提出的卷积深度Q网络算法,对堆叠信息组进行特征张量提取,并通过非稀疏奖励函数塑造技术训练网络,生成高超声速飞行器和防御飞行器的主动防御制导指令;最后,通过数值仿真验证所提出方法的有效性,并在不同信息测量噪声条件下与已有文献方法进行对比分析,证明所提出方法在飞行器逃逸效果上更具优势,并具备更强的鲁棒性。This paper investigates the active defense guidance problem for the hypersonic vehicle.The active defense guidance problem of the hypersonic vehicle is always subject to the limitations of incomplete observation information and observation noise in target-interceptor-defender scenarios.To tackle this issue,this paper introduces a reinforcement learning algorithm and proposes a cooperative active defense guidance based on a convolutional deep Q-network algorithm.In view of the spatiotemporal continuity properties of hypersonic vehicles,a stacking mechanism is proposed to process the incomplete information.The mechanism utilizes temporal dimension extension to compensate for the lack of spatial motion state information.Based on this,the convolutional neural networks are further employed to perform feature extraction on the stacked information.Trained by the shaped continuous reward function,the deep Q-network relies on the extracted feature tensor to obtain guidance.Finally,numerical experiments are performed to demonstrate the performance and robustness of the proposed active defense guidance,comparing it with the documented method.

关 键 词：马尔可夫过程 强化学习 主动防御 制导方法 

分 类 号：V11[航空宇航科学与技术—人机与环境工程]