A review of anti-UAV target detection technology based on differential information

With the rapid proliferation of low-altitude Unmanned Aerial Vehicles (UAVs), unauthorized and disorderly flights have posed persistent threats to public safety, critical infrastructure, and low-altitude airspace management. Stable and reliable detection of small UAVs in complex environments has therefore become a key requirement for anti-UAV systems. Existing reviews are commonly organized according to sensor types or algorithmic architectures, making it difficult to reveal the intrinsic relationships among different sensing modalities. To address this issue, this paper establishes a unified classification framework oriented toward detection criteria from the perspectives of the physical sources and sensing mechanisms of the differential information between UAVs and their backgrounds. Accordingly, anti-UAV target detection is categorized into three types: static appearance differences, dynamic temporal differences, and intrinsic radio-frequency differences. Within this framework, the fundamental principles, representative methods, development trends, advantages, and limitations of single-modal detection technologies, including visible-light imaging, infrared imaging, radar, acoustics, and radio-frequency detection, are systematically reviewed. Multi-modal detection methods are further analyzed in terms of multi-source collaborative modeling, cross-modal alignment and fusion strategies, together with the engineering applications of representative anti-UAV detection systems. In addition, publicly available datasets are summarized according to sensing modality, and commonly used evaluation metrics for different modalities, including detection accuracy, localization accuracy, real-time performance, detection range, and environmental robustness, are reviewed. Finally, future research directions are discussed in light of current technical bottlenecks, including multi-modal collaborative sensing, cross-scenario generalization, robust feature learning, and lightweight deployment.