基于数据辅助的无人机集群协同空域抗干扰

研究移动中的无人机通过动态感知和学习干扰来波方向,实时调整波束成形策略来抑制干扰。针对实际场景中无人机不能获得干扰者的全部动作来进行策略训练的问题,提出使用集群内部协作收集干扰机动作数据从而补充训练数据的方法来从而提升集群抗干扰。将波束成形决策建模为马尔可夫决策过程,基于深度强化学习架构,提出了基于数据辅助的无人机集群协同空域抗干扰算法。仿真结果表明,在辅助数据分别达到40%,60%,80%时,系统吞吐量分别得到33%,55%,70%的提升,验证了本文提出的方法能有效提高无人机协同抗干扰能力。

Cooperative airspace anti-jamming of UAV cluster based on data assistance

This paper studies the moving UAV to suppress the interference by dynamically sensing and learning the interference wave direction, and adjusting the beamforming strategy in real time. Aiming at the problem that the UAV can not obtain all the actions of the jammer for strategy training in the actual scene, a method of using the cooperation within the cluster to collect the action data of the jammer to supplement the training data is proposed to improve the anti-jamming of the cluster. The beamforming decision is modeled as a Markov decision process. Based on the deep reinforcement learning architecture, a data Aided Cooperative spatial anti-jamming algorithm for UAV cluster is proposed. The simulation results show that when the auxiliary data reaches 40%, 60% and 80%, the system throughput is improved by 33%, 55% and 70% respectively. It is verified that the method proposed in this paper can effectively improve the cooperative anti-jamming ability of UAV.