Learned Off-Grid Imager for Low-Altitude Economy with Cooperative ISAC Network
Yixuan Huang, Jie Yang, Shuqiang Xia, Chao-Kai Wen, and Shi Jin

TL;DR
This paper introduces a novel physics-embedded learning approach for low-altitude UAV surveillance using cooperative cellular networks, significantly improving detection accuracy over traditional compressed sensing methods.
Contribution
It proposes a physics-embedded learning method to reduce off-grid errors and an online hard example mining scheme for better UAV detection in low-altitude airspace.
Findings
Achieves 97.55% UAV detection rate
Outperforms traditional CS-based methods
Effectively mitigates off-grid errors
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.…
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Taxonomy
TopicsUAV Applications and Optimization · Sparse and Compressive Sensing Techniques · Advanced SAR Imaging Techniques
