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Air combat maneuvering2/1/2024 ![]() ![]() The rules are constantlychanging what worked yesterday may not work tomorrow, or the latest technology may be defeated by an obsolete system in actual combat conditions. Yintong, L., Han, T., Chu, S., Zhenglei, W.: Optimization method of air combat situation assessment function based on inverse reinforcement learning.The aerial chess game of move and countermove, one plane against another, has been a difficult thing for experts to pin down over the years. 35(5), 1–41 (2007)įeiyan, Z., Linpeng, J., Jun, D.: A review of convolutional neural network research. Yang, G., Shifu, C., Xin, L.: A review of reinforcement learning research. ĭongyuan, H., Rennong, Y., Jialiang, Z., Wanze, Z., Yu, Z., Qiang, Z.: Intelligent Decision-making of unmanned fighter based on LSTM-dueling DQN. Pan, Z., Jiangtao, H., Sheng, Z., Gang, L., Bowen, S., Jigang, T.: Research on intelligent air combat decision and simulation based on deep reinforcement learning. Shengzhe, S., Mengchao, Y., Wei, Z., Chuanqiang, G.: Continuous decision-making method for autonomous air combat. Wen, M.: Research on air combat game decision based on deep reinforcement learning. ![]() Wu, Z.: Research on autonomous maneuvering decision-making method of aircraft based on deep reinforcement learning. Yijia, W., Jun, L., Xiliang, C., Lei, C., Peng, X.: Application of reinforcement learning algorithm in assisted decision-making of over-the-horizon air combat. Nanjing University of Aeronautics and Astronautics (2020) Kangfa, X.: Intelligent decision-making and evaluation method for multi-aircraft collaborative air combat. Wei, S., et al.: Research on the method of multi-machine cooperative air combat based on deep reinforcement learning. Xin, X., Chuan, X.: Research on the application and development of artificial intelligence in UAV systems. Air Force with Tactics in Simulated Operations. Acta Aeronautica et Astronautica Sinica, 43(10) (2022)ĭavid, H.: AI pilot helps U. Xiang, J.W., Dong, X.W., Ding, W.R., et al.: Key technologies for autonomous cooperation of unmanned swarm systems in complex environments. Zhixiao, S., Shengqi, Y., Haiying, P., Chengchao, B., Jun, G.: A review of the future development of intelligent air combat. Keywordsĭianxiong, L., Yuhua, X., Lifeng, W., Dan, Z.: Autonomous transmission technology for air-ground coordination with/unmanned systems. The simulation results show that the agent can effectively perceive the air battlefield situation, maintain the battlefield advantage, and complete the strike against the target. Finally, based on the TD3 algorithm, the fully connected network constructs the agent value network and the target network to realize the agent's decision-making. Secondly, establish a reward function that comprehensively considers the advantage of attack angle, speed, height and distance to guide the agent to converge to the optimal solution. First, establish a UAV flight kinematics/dynamics model to form a one-on-one air combat confrontation situation. ![]() In order to solve the decision-making problem of unmanned aerial vehicles in one-to-one intelligent air combat, an unmanned fighter aircraft decision-making model is established, and the maneuvering decision-making instructions of the intelligent body are solved by deep learning algorithm. ![]() Aircraft intelligent air combat decision-making has become a new research hotspot for military powers in the world. ![]()
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