Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments

• URL: http://arxiv.org/abs/2504.20097v1
• Date: Sat, 26 Apr 2025 07:36:28 GMT
• Title: Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments
• Authors: Junran Guo, Tonglin Mu, Keyuan Li, Jianing Li, Ziyang Luo, Ye Chen, Xiaodong Fan, Jinquan Huang, Minjie Liu, Jinbei Zhang, Ruoyang Qi, Naiting Gu, Shihai Sun,
• Abstract summary: We introduce a novel integration of residual neural networks (ResNet) with textDtextsuperscript2SPtextsuperscript2-LiDAR.<n>Our approach achieves 94.93% pose identification accuracy and 97.99% type classification accuracy, even under weak signal conditions.<n>These findings highlight the potential of imaging-free methods for robust long-range detection of small targets.
• Score: 15.347530074931989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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