TY - GEN
T1 - Dogfight Advantage Occupancy Method Based on Imperfect Information Self-Play
AU - Wang, Dinghan
AU - Ji, Longmeng
AU - Wang, Jingbo
AU - Shi, Zhuoyong
AU - Zhang, Jiandong
AU - Yang, Qiming
AU - Shi, Guoqing
AU - Wu, Yong
AU - Zhu, Yan
AU - Hu, Jinwen
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Air-to-air close combat is a typical combat scenario, which places extremely high physiological demands on pilots during the dogfight process. In order to achieve unmanned and intelligent close combat, this paper proposes a dogfight advantage occupancy algorithm based on imperfect information self-play. Through experiments on the high-fidelity F-16 aircraft platform, the results show that the algorithm can converge to a Nash equilibrium and fully utilize the maneuverability during the combat process.
AB - Air-to-air close combat is a typical combat scenario, which places extremely high physiological demands on pilots during the dogfight process. In order to achieve unmanned and intelligent close combat, this paper proposes a dogfight advantage occupancy algorithm based on imperfect information self-play. Through experiments on the high-fidelity F-16 aircraft platform, the results show that the algorithm can converge to a Nash equilibrium and fully utilize the maneuverability during the combat process.
UR - https://www.scopus.com/pages/publications/85200403632
U2 - 10.1109/ICCA62789.2024.10591896
DO - 10.1109/ICCA62789.2024.10591896
M3 - 会议稿件
AN - SCOPUS:85200403632
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 845
EP - 849
BT - 2024 IEEE 18th International Conference on Control and Automation, ICCA 2024
PB - IEEE Computer Society
T2 - 18th IEEE International Conference on Control and Automation, ICCA 2024
Y2 - 18 June 2024 through 21 June 2024
ER -