co-supported by the National Natural Science Foundation of China(No.52272382);the Aeronautical Science Foundation of China(No.20200017051001);the Fundamental Research Funds for the Central Universities,China.
Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air combat.Although Multi-Agent Reinforcement Learning(MARL)shows outstanding performance ...
supported by the Natural Science Foundation of Shaanxi Province(2020JQ-481,2021JM-224);the Aeronautical Science Foundation of China(201951096002).
The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low ac...
supported by the National Natural Science Foundation of China(No.61573286);the Aeronautical Science Foundation of China(No.20180753006);the Fundamental Research Funds for the Central Universities(3102019ZDHKY07);the Natural Science Foundation of Shaanxi Province(2020JQ-218);the Shaanxi Province Key Laboratory of Flight Control and Simulation Technology。
Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation dur...
supported by the National Natural Science Foundation of China (No. 61573286);the Aeronautical Science Foundation of China (No. 20180753006);the Fundamental Research Funds for the Central Universities (3102019ZDHKY07);the Natural Science Foundation of Shaanxi Province (2019JM-163, 2020JQ-218);the Shaanxi Province Key Laboratory of Flight Control and Simulation Technology。
To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method ba...