Autonomous maneuver decision making is the core of intelligent warfare, which has become the main research direction to enable unmanned aerial vehicles (UAVs) to independently generate control commands and complete air combat tasks according to environmental situation information. In this paper, an autonomous maneuver decision making method is proposed for air combat by two cooperative UAVs, which is showcased by using the typical olive formation strategy as a practical example. First, a UAV situation assessment model based on the relative situation is proposed, which uses the real-time target and UAV location information to assess the current situation or threat. Second, the continuous air combat state space is discretized into a 13 dimensional space for dimension reduction and quantitative description, and 15 typical action commands instead of a continuous control space are designed to reduce the difficulty of UAV training. Third, a reward function is designed based on the situation assessment which includes the real-time gain due to maneuver and the final combat winning/losing gain. Fourth, an improved training data sampling strategy is proposed, which samples the data in the experience pool based on priority to accelerate the training convergence. Fifth, a hybrid autonomous maneuver decision strategy for dual-UAV olive formation air combat is proposed which realizes the UAV capability of obstacle avoidance, formation and confrontation. Finally, the air combat task of dual-UAV olive formation is simulated and the results show that the proposed method can help the UAVs defeat the enemy effectively and outperforms the deep Q network (DQN) method without priority sampling in terms of the convergence speed.

Unmanned aerial vehicles (UAVs) have been found significantly important in the air combats, where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics. The key to empower the UAVs with such capability is the autonomous maneuver decision making. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. First, based on the process of air combat and the constraints of the swarm, the motion model of UAV and the multi-to-one air combat model are established. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. The results show that the algorithm is suitable for UAV swarms of different scales.

Unmanned aerial vehicle (UAV) technology are widely utilized, but in a non-satellite navigation environment or a weak satellite navigation environment, the reliability of unmanned aerial vehicles drops severely, and this is especially the case for swarms. In order to achieve swarm navigation under the condition of no satellite navigation, this paper have developed a small but fully autonomous UAV based on visual sensors. Then, aiming at the problem that the positioning error continuously increases when only relying on visual positioning during the flight of multiple UAVs, this paper proposes an inter-aircraft positioning mutual correction algorithm based on Ultra Wideband (UWB) ranging. In a constrained environment, the stable maintenance of the swarm formation can also be achieved. A variety of real-world field experiments have proved the scalability of our system and the feasibility of the inter-aircraft correction technology.