Learning-based strategies optimize the model and structural parameters by means of online learning or training data, which mainly includes artificial immune system, genetic algorithm, heuristic learning algorithm, neural network method, deep reinforcement learning (DRL), etc. In short, rule-based strategies are monotonous and rigid, which can not adapt to the complex and highly dynamic air combat scenarios, and can not meet the requirements of intelligent operations. The two methods introduced in can’t be applied to the air combat scene without model or with incomplete environment information, because these methods need to accurately model and describe the strategy model. Moreover, influence diagram method relies on prior knowledge, and the algorithm reasoning process is cumbersome, which can not meet the requirements of real-time and high dynamic air combat. In and, multi-level influence graph can realize multiple-to-one air combat, but it is only suitable for small-scale UAV swarms. When UAV can not find the appropriate strategy scheme in the rule base, it must introduce human intervention. Moreover, the UAVs cannot make dicision independently by using expert system method. Expert experience is difficult to cover all air combat situations, so is very complex to establish rule base and constraint conditions by using expert system method. On the other hand, this method has reward delay in the sequential decision making, which does not have the ability of long-term planning. On the one hand, the score function used in this method is difficult to design, which can not accurately discribe the actual air combat. The matrix game method is prone to the phenomenon that the overall strategy effect is not good. Rule-based strategy mainly select actions according to the given behavior rules in air combat, and not need online training and optimization, including matrix game algorithm, expert system, influence graph method, differential game method, etc. The existing air combat maneuver strategies can be divided into two categories: rule-based strategy and learning-based strategy. ![]() Swarms beyond visual range air combat refers to the situation assessment, environment awareness, and maneuver strategy of UAVs through sensing or detection equipment, and maneuver strategy is the basis of the above tasks. ![]() With the increase of the operating range of airborne detection equipment, the scope of modern air combat has gradually developed from line of sight to beyond line of sight. Due to the limitations of single UAV’s mission and combat capability, the swarms and intelligence of unmanned combat have become a research hotspot in recent years. Unmanned aerial vehicle (UAV) with the characteristics of low cost, strong mobility, high concealment and no need of pilot control, have been more and more widely used to replace manned aircraft to perform military tasks such as detection, monitoring and target strike, and is a typical representative of “non-contact” combat equipment. The results show that the algorithm is suitable for UAV swarms of different scales. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. ![]() Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. 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. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. The key to empower the UAVs with such capability is the autonomous maneuver decision making. 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.
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