Manual-Based Automated Maneuvering Decisions for Air-to-Air Combat

Abstract

A novel new air combat algorithm is proposed, which is based on the knowledge extracted from the experience of human pilots. First, to implement a fighter that maneuvers based on manual control, the maneuver form of the fighter is analyzed and represented as a block. Second, the blocks for each function are connected based on their relationship, and a flow diagram is presented according to the engagement situation of the adversary and ownship. Third, a behavior tree model is applied as a decision-making model to implement the flow diagram as a simulation program. The behavior tree offers good scalability because nonleaf nodes can be added when sophisticated and complex decision-making is required. The proposed method has the advantage of making all maneuvers performed by the algorithm understandable and interpretable. Additionally, it can replace expensive and dangerous dogfighting training for student pilots because the proposed model can emulate maneuvers that manned pilots would perform. To verify the proposed method, the evaluation criteria from the AlphaDogfight Trials are equally applied in the simulation. The experimental results demonstrate that the proposed method has superior engagement capability as compared to the existing air-to-air combat models.