弹道学报2025，Vol.37Issue(4)：38-47,10.DOI:10.12115/ddxb.2025.10007
基于深度学习与动力学约束的高速飞行器轨迹预测方法
Trajectory Prediction Method for High-speed Vehicles Based on Deep Learning and Dynamic Constraints
摘要
Abstract
To meet the real-time forecasting requirements of highly dynamic and strongly nonlinear trajectories in supersonic vehicle interception missions,and to address the limitations of traditional trajectory prediction methods,such as the lack of physical constraints,insufficient long-term temporal feature extraction,and weak generalization capability,this study proposes a hybrid-driven prediction method that integrates deep feature representation with dynamic constraints.Firstly,a three degree-of-freedom reentry dynamic model of the supersonic vehicle is established,and the effects of aerodynamic parameters and bank-angle commands on state evolution are analyzed.Secondly,a feature representation network based on a convolutional-bidirectional long short-term memory-self-attention architecture is designed to extract temporal dependencies and internal correlations of flight states.Finally,the parameters predicted by the network are incorporated as driving terms into the dynamic equations,forming a closed-loop recursive framework of"parameter prediction-state update-trajectory generation",thereby achieving high-accuracy prediction through the joint driving of data and physical laws and ensuring consistency with the underlying motion dynamics.Simulation results show that the proposed method maintains high prediction accuracy and stability across different scenarios,with single-step inference remaining at the millisecond level,fully meeting real-time requirements.Moreover,it demonstrates strong generalization capability when applied to non-cooperative target trajectories,providing a feasible technical approach for real-time trajectory forecasting of supersonic vehicles.关键词
高速飞行器/轨迹预测/混合驱动模型/深度学习/动力学约束Key words
high-speed vehicle/trajectory prediction/hybrid-driven model/deep learning/dynamic constraints分类
军事科技引用本文复制引用
张靖岩,赵斌,于知涵,卢青,蒋瑞民..基于深度学习与动力学约束的高速飞行器轨迹预测方法[J].弹道学报,2025,37(4):38-47,10.基金项目
国家自然科学基金项目(62373307) （62373307）
国家级大学生创新训练项目(202410699200) （202410699200）
陕西省自然科学基础研究计划资助项目(2025JC-YBQN-585) （2025JC-YBQN-585）
中央高校基本科研业务费专项资金资助 （）
