Volume 38, Number 6, December 2020
|1330 - 1338
|02 February 2021
Air Combat Maneuver Decision Based on Reinforcement Genetic Algorithm
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2 China Aeronautical Radio Electronics Research Institute, Shanghai 200241, China
3 School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China
With the continuous development of UAV technology, the trend of using UAV in the military battlefield is increasingly obvious, but the autonomous air combat capability of UAV needs to be further improved. The air combat maneuvering decision is the key link to realize the UAV autonomous air combat, and the genetic algorithm has good robustness and global searching ability which is suitable for solving large-scale optimization problems. This paper uses an improved genetic algorithm to model UAV air combat maneuvering decisions. Based on engineering application requirements, a typical simulation test scenario is established. The simulation results show that the air combat maneuvering decision model based on reinforcement genetic algorithm in this paper can obtain the correct maneuvering decision sequence and gain a position advantage in combat.
Key words: air combat maneuvering decision / genetic algorithm / reinforcement learning / control and decision / UAV / model / simulation test scenario
关键字 : 空战机动决策 / 遗传算法 / 强化学习 / 控制与决策
© 2020 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.