An Auction-based Attack-defense Decision-making Method for UAV Air Combat

With the rise of applications of Unmanned Aerial Vehicle (UAV) swarms in warfare, the offensive and defensive confrontation between UAV swarms has become an important form of combat. This paper proposes an attack-defense algorithm for fixed-wing UAVs' target-attack-defense tripartite swarm adversarial decision-making. An auction algorithm based on the Dubins path value function which decouples the swarm attack-defense confrontation problem into target-attack-defense differential games is integrated in the proposed algorithm. In order to making the algorithm more practical, it takes constraints about body frame transformation and autopilot control into account. A series of numerical experiments with different swarm scale, individual speed, and acceleration have been conducted. Numerical experiments demonstrate the ability of ensuring individual optimality and guaranteeing cooperative behaviors of the swarm, and the scalability of the proposed algorithm. Several factors on combat result are further investigated, and conclusions about initial position, individual velocity and maximum acceleration are identified.