一种无人机集群对抗多耦合任务智能决策方法

针对复杂场景下无人机集群对抗中协同目标分配和突防轨迹规划等多耦合任务的决策问题，提出了一种集群对抗多耦合任务智能决策方法。首先，针对无人机集群对抗中耦合任务多和决策空间大难题，结合集中式和分层式架构的优点，设计了面向多耦合任务的混合式深度强化学习架构，可提升多耦合任务间的协同性和集群对抗效能；其次，针对轨迹规划序贯决策的稀疏奖励难题，设计了基于轨迹构造的一步式动作空间设计方法，可加快策略网络收敛速度；再次，针对强对抗条件下的场景不确定难题，基于无人机集群红蓝对抗仿真平台，设计了基于多随机场景的红蓝博弈训练方法，可增强策略网络的泛化性；最后，通过与传统方法、集中式架构方法和分层式架构方法进行对比，验证了此方法的有效性和先进性。

An Intelligent DecisionMaking Method for MultiCoupling Tasks ofUAV Cluster Countermeasure

Aiming at the decision making problems of multi coupling tasks such as cooperative target assignment and penetration trajectory planning in UAV cluster countermeasure in complex scenes, an intelligent decision making method for multi coupling tasks in UAV cluster countermeasure is proposed. Firstly, aiming at the problems of multi coupling tasks and large decision making space in UAV cluster countermeasure, combined with the advantages of centralized and hierarchical architectures, a hybrid deep reinforcement learning architecture for multi coupling tasks is designed, which can improve the cooperation between the multi coupling tasks and the effectiveness of cluster countermeasure. Secondly, for the sparse reward problem of sequential decision making in trajectory planning, a trajectory construction method is designed. Thirdly, aiming at the scene uncertainty problem under the strong countermeasure conditions, based on the UAV cluster red blue countermeasure simulation platform, a red blue game training method based on multiple random scenes is designed, which can enhance the generalization of the strategy network. Finally, by comparing with the traditional method, the centralized architecture method and the hierarchical architecture method, the simulation results show that the effectiveness and the advanced nature of the proposed method are verified.