航空科学技术2024，Vol.35Issue(3)：111-118,8.DOI:10.19452/j.issn1007-5453.2024.03.013
基于深度强化学习的多无人机空战机动策略研究
Research on Multi-UAV Air Combat Maneuver Strategy Based on Deep Reinforcement Learning
摘要
Abstract
In face of the incoming attack of enemy air power,UAVs with autonomous coordination and flexible maneuvering capability are an important force to participate in air combat.Facing the demand of confrontation combat mission with high winning rate of multi-UAV coordination,and based on the number of air combat targets,we focus on the research of multi-UAV to single-target coordinated air combat maneuver strategy and multi-UAV to multi-target coordinated air combat maneuver strategy.This paper mainly analyzes the key battlefield elements in the process of air combat,and establishes the UAV motion model based on the characteristics of multi-machine maneuver.According to the fire control characteristics of UAV,analyze the change rule of UAV state,establish UAV attack model and dynamic confrontation model against the enemy;for the problem of multi-UAV to single-target autonomous coordinated aerial combat,put forward multi-autonomous maneuver strategy based on the combination of expert rules and reinforcement learning.The simulation results show that the proposed algorithm can accomplish the task of multi-aircraft aerial combat against single target with real-time change of situation.Under the premise of the same number of combatants,if the enemy does not have intelligent maneuvering behavior,our victory rate is 100%.Even if both sides use the same strategy,if our number is more than the enemy,we still have a large victory rate.This demonstrates the effectiveness of the coordinated strategy.关键词
空战策略/强化学习/自主机动/多机协同/态势评估Key words
air combat strategy/reinforcement learning/autonomous mobility/multiple machine collaboration/situation assessment分类
航空航天引用本文复制引用
雷毅飞,王露禾,贺泊茗,胡劲文,徐钊,吕明伟,徐港..基于深度强化学习的多无人机空战机动策略研究[J].航空科学技术,2024,35(3):111-118,8.基金项目
国家自然科学基金(61803309) （61803309）
航空科学基金(2019ZA053008,20185553034) （2019ZA053008,20185553034）
陕西省重点研发计划项目(2020ZDLGY06-02) （2020ZDLGY06-02）
中国博士后科学基金(2018M633574) National Natural Science Foundation of China(61803309) （2018M633574）
Aeronautical Science Foundation of China(2019ZA053008,20185553034) （2019ZA053008,20185553034）
Key Research and Development Program Projects of Shaanxi Province(2020ZDLGY06-02) （2020ZDLGY06-02）
China Post-doctoral Science Foundation(2018M633574) （2018M633574）
