发表机构
Queen’s University; Universidade Federal da Paraíba; Czech Technical University in Prague（女王大学; 帕拉伊巴联邦大学; 布拉格捷克技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对GPS和通信受限环境下地面目标保护问题，提出用无人机自主群的方法，开发卡尔曼滤波器并提出分布式群包围技术，经真实机器人验证，可有效检测、包围和拦截敌对无人机。
AI 中文摘要
军事行动中无人机的出现增加了对防御无人机攻击系统的需求，无人机也可作为应对措施。多数现有方法依赖无人机间通信和全球定位，但现代战争场景中这些资源可能不可用。为此，我们提出利用无人机自主群对地面目标进行保护的流程。假设处于通信和GPS受限环境，无人机利用机载传感器跟踪目标并群内协调。我们开发卡尔曼滤波器仅用相对测量估计未知目标状态和无人机位置，还提出适应目标运动的分布式群包围技术。通过真实机器人广泛验证，证明该方法在检测、包围和拦截敌对无人机方面有效。
英文摘要
The presence of UAVs in military operations has recently increased, also increasing the demand for defense systems against UAV attacks. UAVs can also be used as countermeasures. Most available methods rely on UAV-to-UAV communication and global positioning. However, such resources may not be available in modern warfare scenarios. To address these limitations, we propose a pipeline for ground-target protection against UAV attacks that employs autonomous swarms of UAVs. We assume a communication- and GPS-denied environment in which the UAVs use onboard sensors to track the target and coordinate as a swarm. We developed Kalman filters to estimate the states of unknown targets and the positions of UAVs in the swarm using only relative measurements. Also, our strategy is to encircle the target of interest to maximize coverage. To achieve that, we propose a decentralized swarm encirclement technique that adapts to the target's motion. Our approach was extensively validated using real robots, demonstrating its effectiveness in detecting, encircling, and intercepting hostile UAVs.
CommentsAccepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
