【摘要】 随着无人机技术在低空环境中的广泛应用，低空无人机小目标探测面临日益严峻的挑战。低空无人机通常具有噪声低、雷达散射截面小、机动性强等特点，但在飞行过程中易受地形、建筑物、树林等复杂背景的遮挡与干扰，导致现有探测技术的识别效能显著下降。本文系统综述了当前主流的无人机小目标探测技术，详细分析了声波探测、雷达探测、射频探测和光电探测等手段在不同应用场景下的技术优势与局限性。在此基础上，重点介绍了一种新型光电探测技术——调制-解调型快照式光谱成像技术的发展历程，并探讨其在无人机探测应用中的潜在优势与面临的技术挑战，旨在为未来低空无人机探测技术的多模态高效融合提供技术基础与发展思路。更多还原

【Abstract】 Significance The extensive application of UAV technology in low-altitude environments has posed new challenges for UAV target detection in complex backgrounds. While conventional detection methods such as acoustic detection, radar detection, and electro-optical detection have achieved certain success in UAV identification, they still present multiple limitations. Contemporary low-altitude UAVs feature low noise levels, small radar cross-sections, and vulnerability to obstruction interference from trees and buildings, significantly increasing detection difficulties. Consequently, there is an urgent need for novel electro-optical solutions to effectively detect UAV targets in long-range, complex scenarios. The rapidly developing modulated-demodulated snapshot spectral imaging technology can simultaneously acquire both spatial and spectral information of targets through single-exposure imaging. By leveraging the spectral feature differences between targets and their backgrounds, this technology enables effective identification, thereby providing a new electro-optical approach for low-altitude drone detection in complex environments.Progress Currently, UAV target-detection technology in complex environments has advanced rapidly, establishing a multimodal detection system encompassing acoustic, radar, radio-frequency(RF), and electro-optical(infrared/visible) approaches. Meanwhile, integrated multi-technology solutions have matured, collectively forming a comprehensive framework for small-UAV detection and identification. In acoustic detection, sensor design has evolved from singlemicrophone to multi-microphone arrays, with technological progression shifting from single-point sensing to spatial sampling, and from shallow feature extraction to deep feature representation. The radar-detection domain has achieved breakthroughs through deep integration of millimetre-wave radar with deep-learning algorithms, effectively addressing lowaltitude small-UAV detection challenges by enhancing micro-Doppler feature extraction and optimizing anti-interference strategies. RF detection technology is advancing through the combination of deep-learning frameworks and specialised RF datasets, where deep neural networks(DNNs) and residual convolutional neural networks(RCNNs) have significantly improved recognition accuracy and environmental adaptability. For visual detection modalities such as visible-light and infrared imaging, optimisation primarily leverages YOLO-series object-detection models, with notable progress in model simplification, lightweight design, and computational efficiency. Modulated-demodulated snapshot spectral imaging has emerged as a mature electro-optical solution. This team has developed a snapshot-based small-UAV detection system using this technology and has successfully achieved effective target identification. Experimental results demonstrate an algorithm accuracy of 0.953 and a recall rate of 0.948, significantly enhancing detection robustness for small-UAV targets in complex backgrounds.Conclusions and Prospects The rapid development of modulated-demodulated snapshot spectral imaging technology has provided crucial technical support for spectral detection of dynamic targets, significantly enhancing the detection capability for point targets such as low-altitude drones. Meanwhile, as a core technology, this electro-optical fusion-detection approach will continue to evolve, driving deep integration and fusion of multispectral, infrared and low-light imaging technologies in the feature domain. By achieving multimodal fusion based on electro-optical information, we can further improve the detection performance and recognition accuracy for small low-altitude UAV targets. To meet the demands of target tracking and countermeasures, future efforts will focus on synergistic integration of electro-optical detection with radar and other sensing technologies, ultimately establishing a more efficient and robust comprehensive monitoring system.更多还原