系统梳理反无人机技术，覆盖检测、追踪与未来方向。
Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions
- 整合多模态数据与新型学习方法提升反无人机能力
- 发现实时性、隐蔽目标与集群场景仍存显著短板
- 适合安全防护与智能感知领域研究者参考
无人驾驶飞行器（UAV）在基础设施巡检、监控等任务中不可或缺，但也带来严峻安全挑战。本文全面综述反无人机领域，聚焦分类、检测与追踪三大核心目标，详述基于扩散模型的数据生成、多模态融合、视觉-语言建模、自监督学习及强化学习等新兴方法。系统评估了单模态与多传感器流水线（涵盖RGB、红外、音频、雷达、射频）的前沿方案，并讨论大规模与对抗性基准。分析揭示实时性能、隐蔽目标检测及群体场景应对仍存在明显不足，凸显对鲁棒、自适应反无人机系统的需求。通过指明开放研究方向，旨在推动创新，引导下一代防御策略的发展。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) are indispensable for infrastructure inspection, surveillance, and related tasks, yet they also introduce critical security challenges. This survey provides a wide-ranging examination of the anti-UAV domain, centering on three core objectives-classification, detection, and tracking-while detailing emerging methodologies such as diffusion-based data synthesis, multi-modal fusion, vision-language modeling, self-supervised learning, and reinforcement learning. We systematically evaluate state-of-the-art solutions across both single-modality and multi-sensor pipelines (spanning RGB, infrared, audio, radar, and RF) and discuss large-scale as well as adversarially oriented benchmarks. Our analysis reveals persistent gaps in real-time performance, stealth detection, and swarm-based scenarios, underscoring pressing needs for robust, adaptive anti-UAV systems. By highlighting open research directions, we aim to foster innovation and guide the development of next-generation defense strategies in an era marked by the extensive use of UAVs.
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