Cooperative Decision-Making of UAVs Within-Visual-Range Integrating a Two-Step Matrix Game and an Improved Genetic Algorithm

The effectiveness of autonomous decision-making of unmanned aerial vehicles (UAVs) exceeds that of a single one. However, existing methods face critical gaps in within-visual-range (WVR) combat: single-step strategies lack decision depth and precision; non-cooperative multi-UAV frameworks lose tactical advantages-both conflicting with WVR's stringent demands for rapid response and high precision. To bridge these issues, an UAV autonomous decision-making model is proposed. This model incorporates several key components, including a dynamics model with overload control, a relative motion model, an overload maneuver library, and a situation assessment (SA) model. To systematically address the decision-making challenge in WVR engagements, a two-step strategy is adopted to design a 1v1 decision-making method. Specifically, in the first step, preliminary maneuver selection is made using an improved matrix decision-making method, which incorporates a Kalman filter for improved accuracy. Subsequently, the second step involves reoptimizing the decision using an improved genetic algorithm (IGA). Building upon this 1v1 decision-making framework, a 2v1 cooperative decision-making method is proposed-leveraging distributed cooperative principles and information exchange mechanisms. The simulation results show that, compared to the single-step strategy method, the proposed two-step strategy matrix decision-making method significantly reduces the time required to lock onto the enemy and increases tactical advantage. In addition, the proposed cooperative decision-making method outperforms non-cooperative approaches, both in terms of locking time and advantage. The proposed cooperative decision-making framework for UAVs enables significantly enhance efficiency and precision, offering advancements in autonomous operations and real-time decision-making systems.