守护天空：反无人机方法的全面调查、基准测试与未来方向
Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions
AI总结：
本文综述了反无人机领域，探讨了分类、检测与跟踪等核心方法，分析了数据合成、多模态融合等新兴技术，并指出实时性能、 stealth 检测等领域的不足，旨在推动下一代防御策略的发展。
摘要：
无人驾驶飞行器（UAVs）在基础设施检查、监视等任务中不可或缺，但也带来重大安全挑战。本文全面调查反UAV领域，聚焦分类、检测与跟踪三大核心目标，详细介绍了扩散数据合成、多模态融合、视觉-语言模型等新兴方法。系统评估了单模态和多传感器管道（涵盖RGB、红外、音频、雷达和RF）的最先进解决方案，并讨论了大规模及对抗性基准测试。分析揭示了实时性能、 stealth 检测及蜂群场景等持续存在的差距，强调了对稳健、自适应反UAV系统的需求。通过突出开放研究方向，本文旨在促进创新并指导下一代防御策略的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
