Detecting small objects, such as drones, over long distances presents a significant challenge with broad implications for security, surveillance, environmental monitoring, and autonomous systems. Traditional imaging-based methods rely on high-resolution image acquisition, but are often constrained by range, power consumption, and cost. In contrast, data-driven single-photon-single-pixel light detection and ranging (\text{D\textsuperscript{2}SP\textsuperscript{2}-LiDAR}) provides an imaging-free alternative, directly enabling target identification while reducing system complexity and cost. However, its detection range has been limited to a few hundred meters. Here, we introduce a novel integration of residual neural networks (ResNet) with \text{D\textsuperscript{2}SP\textsuperscript{2}-LiDAR}, incorporating a refined observation model to extend the detection range to 5~\si{\kilo\meter} in an intracity environment while enabling high-accuracy identification of drone poses and types. Experimental results demonstrate that our approach not only outperforms conventional imaging-based recognition systems, but also achieves 94.93\% pose identification accuracy and 97.99\% type classification accuracy, even under weak signal conditions with long distances and low signal-to-noise ratios (SNRs). These findings highlight the potential of imaging-free methods for robust long-range detection of small targets in real-world scenarios.
View on arXiv@article{guo2025_2504.20097,
title={ Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments },
author={ Junran Guo and Tonglin Mu and Keyuan Li and Jianing Li and Ziyang Luo and Ye Chen and Xiaodong Fan and Jinquan Huang and Minjie Liu and Jinbei Zhang and Ruoyang Qi and Naiting Gu and Shihai Sun },
journal={arXiv preprint arXiv:2504.20097},
year={ 2025 }
}