基于态势认知的无人机集群围捕方法

无人机集群围捕是智能无人机“蜂群”作战的一种重要任务方式。现有集群围捕方法大多建立在环境已知的基础上，面对未知的任务环境时围捕策略经常性失效。针对此问题，提出了基于态势认知的发育模型，探索一种对环境适应性较佳的围捕方法。首先，对集群围捕行为分解，将围捕离散化；然后，基于深度Q神经网络（DQN），设计一种围捕策略的生成方法；最后，建立状态-策略知识库，并通过大量有效数据的训练，针对不同环境获得不同的策略，对知识库进行发育。仿真结果表明:提出的基于态势认知的发育模型，能够有效适应不同环境，完成不同环境下的围捕。 

Swarm rounding up method of UAV based on situation cognition

UAV swarm rounding up is an important mission mode of intelligent UAV swarm operation. Most of the existing swarm rounding up methods are based on the known environment, and the strategy often fails in the face of unknown mission environment. To solve this problem, a developmental model based on situation cognition is proposed in this paper to explore a better adaptive method of rounding up. First, the swarm rounding up behavior is decomposed and the rounding up is discretized. Then, based on the Deep Q-Network (DQN), a method of generating the rounding up strategy is designed. Finally, the state-strategy knowledge base is established, and through the training of a large amount of effective data, different strategies are obtained according to different environments to develop the knowledge base. The simulation results show that the proposed developmental model based on situation cognition can effectively adapt to different environments and complete the rounding up in different environments.