Layered Autonomous Decision Framework and DDQN-Enhanced Training for the BVR Air Game
Year: 2024
Type: article
Abstract: Modern beyond visual range air game is characterized by high dynamic complexity and multi-factor interweaving, making it challenging for traditional decision-making methods. This paper constructs an autonomous decision-making framework for a single-plane beyond visual range air game that encompasses the entire game process. We design tactical rules with both offensive and defensive capabilities and formulate an autonomous air game decision-making method. This method is primarily driven by rules and is complemented by an intelligent maneuver decision-making approach based on the Double Deep Q-Network (DDQN). Simulation results demonstrate that our designed method effectively enhances the maneuvering decision-making ability of aircraft and has promising application prospects.
Source: Guidance Navigation and Control
Authors: Wenfei Wang, Le Ru, Maolong Lv, Jiaheng Tang, Hengzhi Fan
Institution: Air Force Engineering University
Language: English
Topic: Guidance and Control Systems
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405871526