Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments
Journal:
arXiv
Published Date:
Apr 26, 2025
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.
