关键词: 无人机集群, 干扰管理, 多源数据融合, 通信导航感知一体化

Abstract: With the rapid integration of low-altitude economy into national strategic emerging industries development plans, unmanned aerial vehicle (UAV) swarms, leveraging their distributed collaboration capabilities, have emerged as a pivotal technological paradigm to overcome the sensing limitations and computational bottlenecks of single-UAV systems. Operating in autonomous or semi-autonomous modes, UAV swarms achieve enhanced mission performance through dynamic networking, data sharing, and task coordination. These advancements have unlocked unprecedented efficiency in applications such as large-scale remote sensing monitoring, urban logistics delivery, and disaster-induced 3D reconstruction, surpassing the operational limits of individual UAV systems. However, with the large-scale deployment of UAVs, the interference effects faced by UAV swarms have become increasingly complex. This includes not only the expansion of electromagnetic interference due to spectrum overlap and dense communication links, but also the spatiotemporal inconsistency of heterogeneous sensing data caused by sensor diversity and transmission latency, as well as the reduced adaptability of UAV swarms to dynamic environments influenced by weather and terrain variations. These interference factors interact and accumulate across the communication, sensing, and control functions of UAVs, forming complex interference effects that undermine the robustness of UAV swarm task execution and hinder their application in high-reliability scenarios. This paper systematically analyzes interference from communication, sensing, and environmental factors, and proposes targeted interference management mechanisms across the communication, sensing, and control layers of UAV swarms. We consolidate a technology framework encompassing both individual UAV and swarm-level collaborative anti-interference strategies, critically evaluate the state-of-the-art approaches, and identify their limitations. Furthermore, we highlight unresolved challenges and propose future research directions, aiming to provide theoretical foundations and technical guidelines for building highly reliable UAV swarm systems.

Key words: UAV Swarm, Interference Management, Multi-Source Data Fusion, Integration of Communication,, Navigation,, and Control