Learned Off-Grid Imager for Low-Altitude Economy with Cooperative ISAC Network
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
The low-altitude economy is emerging as a key driver of future economic
growth, necessitating effective flight activity surveillance using existing
mobile cellular network sensing capabilities. However, traditional monostatic
and localizationbased sensing methods face challenges in fusing sensing results
and matching channel parameters. To address these challenges, we model
low-altitude surveillance as a compressed sensing (CS)-based imaging problem by
leveraging the cooperation of multiple base stations and the inherent sparsity
of aerial images. Additionally, we derive the point spread function to analyze
the influences of different antenna, subcarrier, and resolution settings on the
imaging performance. Given the random spatial distribution of unmanned aerial
vehicles (UAVs), we propose a physics-embedded learning method to mitigate
off-grid errors in traditional CS-based approaches. Furthermore, to enhance
rare UAV detection in vast low-altitude airspace, we integrate an online hard
example mining scheme into the loss function design, enabling the network to
adaptively focus on samples with significant discrepancies from the ground
truth during training. Simulation results demonstrate the effectiveness of the
proposed low-altitude surveillance framework. The proposed physicsembedded
learning algorithm achieves a 97.55% detection rate, significantly
outperforming traditional CS-based methods under off-grid conditions. Part of
the source code for this paper will be soon accessed at
https://github.com/kiwi1944/LAEImager.
