A dynamic grid planning environment is designed, which can make the UAV adaptively adjust the grid size according to the changing air combat situation, and has no impact on the solution rate. Based on the Q-learning algorithm, the lateral maneuver decision-making model of UAV air combat is constructed, and it is verified that the improved Q-learning algorithm can play a significant role in improving the winning / losing ratio of UAV air combat through the exchange of weapon platforms.
UAV Air Combat Maneuver Decision Algorithm Based on Dynamic Grid Planning Environment
23.09.2022
352409 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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