现代防御技术2026，Vol.54Issue(3)：71-81,11.DOI:10.3969/j.issn.1009-086x.2026.03.007
基于强化学习的无人攻击机与诱饵机协同航迹规划
Reinforcement Learning-Based Cooperative Trajectory Planning for Unmanned Combat Aerial Vehicles and Decoy UAVs
祁昊哲 1郑明发 2胡小荣 3杨楠1
作者信息
- 1. 空军工程大学 空管领航学院,陕西 西安 710051
- 2. 空军工程大学 基础部,陕西 西安 710051
- 3. 军事科学院 国防科技创新研究院,北京 100071
- 折叠
摘要
Abstract
Unmanned aerial vehicle(UAV)cooperative combat is crucial in modern warfare.The cooperative mode between unmanned combat aerial vehicles(UCAVs)and decoy UAVs has gained significant attention due to its tactical value.This paper proposes a cooperative trajectory planning method based on the proximal policy optimization(PPO)algorithm for UCAV and decoy UAV strike missions against key enemy targets.We construct a Markov decision process(MDP)model incorporating dynamic threat assessment,integrating UAV kinematics and battlefield constraints,and design the state/action spaces and a hierarchical reward function.Simulation results demonstrate that the proposed method effectively guides UCAVs and decoys to achieve efficient cooperation in complex environments,significantly increasing mission success rates while reducing interception risks from enemy air defense systems.This provides theoretical and technical support for intelligent path planning in UAV cooperative operations.关键词
无人机协同作战/航迹规划/强化学习/PPO算法/马尔可夫决策Key words
UAVs cooperative operations/trajectory planning/reinforcement learning/proximal policy optimization(PPO)分类
航空航天引用本文复制引用
祁昊哲,郑明发,胡小荣,杨楠..基于强化学习的无人攻击机与诱饵机协同航迹规划[J].现代防御技术,2026,54(3):71-81,11.
