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.

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 single-microphone 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 low-altitude 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.

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.