In recent years, Unmanned Aerial Vehicles (UAVs) have brought a new true revolution to military tactics. While UAVs already constitute an advantage when operating alone, multi-UAV swarms expand the available possibilities, allowing the UAVs to collaborate and support each other as a team to carry out a given task. This entails the capability to exchange information related with situation awareness and action coordination by means of a suitable wireless communication technology. In such scenario, the adversary is expected to disrupt communications by jamming the communication channel. The latter becomes the Achilles heel of the swarm. While anti-jamming techniques constitute a well covered topic in the literature, the use of intelligent swarm behaviors to leverage those techniques is still an open research issue. This paper explores the use of Genetic Algorithms (GAs) to jointly optimize UAV swarm formation, beam-steering antennas and traffic routing in order to mitigate the effect of jamming in the main coordination channel, under the assumption that a more robust and low data rate channel is used for formation management signaling. Simulation results show the effectiveness of proposed approach. However, the significant computational cost paves the way for further research.

翻译：近年来，无人机为军事战术带来了真正的革命性变革。虽然单架无人机在独立作战时已具备优势，但多无人机集群进一步拓展了应用可能，使无人机能够作为团队相互协作与支援以执行特定任务。这要求集群能够通过合适的无线通信技术交换态势感知与行动协调相关信息。在此类场景中，敌方预期会通过干扰通信信道来破坏通信，这使得通信链路成为集群的致命弱点。尽管抗干扰技术在现有文献中已有充分研究，但如何利用智能集群行为来增强这些技术仍是待解决的研究课题。本文探索使用遗传算法联合优化无人机集群编队、波束赋形天线与流量路由，以减轻主协调信道所受的干扰影响，其前提是采用更稳健的低数据率信道进行编队管理信令传输。仿真结果表明了所提方法的有效性。然而，显著的计算成本为后续研究指明了方向。