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.

翻译：无人机在基础设施检测、监控及相关任务中不可或缺，但也带来了关键的安全挑战。本综述对反无人机领域进行了广泛考察，聚焦于分类、检测与跟踪三大核心目标，并详细阐述了新兴方法，如基于扩散的数据合成、多模态融合、视觉-语言建模、自监督学习与强化学习。我们系统评估了单模态与多传感器（涵盖RGB、红外、音频、雷达与射频）流程中的前沿解决方案，并讨论了大规模及对抗性导向的基准测试。分析揭示了在实时性能、隐身检测与集群场景方面存在的持续差距，突显了对鲁棒、自适应反无人机系统的迫切需求。通过强调开放研究方向，我们旨在推动创新，并指导在无人机广泛应用时代下下一代防御策略的开发。