飞控与探测2025，Vol.8Issue(1)：47-56,10.DOI:10.20249/j.cnki.2096-5974.2025.01.006
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
摘要
Abstract
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 scenari-os.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 fea-ture 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 docu-mented method.关键词
马尔可夫过程/强化学习/主动防御/制导方法Key words
Markov process/reinforcement learning/active protection/guidance law引用本文复制引用
倪炜霖,丘沛桓,柳明军,曾景岚,梁海朝..信息非完备下飞行器智能主动防御制导方法[J].飞控与探测,2025,8(1):47-56,10.基金项目
国家自然科学基金(62003375,62103452) （62003375,62103452）
