Cooperative ISAC Network for Off-Grid Imaging-based Low-Altitude Surveillance

TL;DR

This paper presents a cooperative imaging-based low-altitude surveillance system using mobile networks, combining compressed sensing, physics-embedded learning, and online hard example mining to detect UAVs effectively.

Contribution

It introduces a novel cooperative radio imaging approach with physics-embedded learning to address off-grid issues and improve UAV detection in low-altitude surveillance.

Findings

Physics-embedded learning outperforms traditional CS methods under off-grid conditions.

The cooperative imaging approach effectively detects UAVs in low-altitude space.

The online hard example mining enhances detection of rare UAV instances.

Abstract

The low-altitude economy has emerged as a critical focus for future economic development, emphasizing the urgent need for flight activity surveillance utilizing the existing sensing capabilities of mobile cellular networks. Traditional monostatic or localization-based sensing methods, however, encounter challenges in fusing sensing results and matching channel parameters. To address these challenges, we propose an innovative approach that directly draws the radio images of the low-altitude space, leveraging its inherent sparsity with compressed sensing (CS)-based algorithms and the cooperation of multiple base stations. Furthermore, recognizing that unmanned aerial vehicles (UAVs) are randomly distributed in space, we introduce a physics-embedded learning method to overcome off-grid issues inherent in CS-based models. Additionally, an online hard example mining method is incorporated into the design of the loss function, enabling the network to adaptively concentrate on the samples bearing significant discrepancy with the ground truth, thereby enhancing its ability to detect the rare UAVs within the expansive low-altitude space. Simulation results demonstrate the effectiveness of the imaging-based low-altitude surveillance approach, with the proposed physics-embedded learning algorithm significantly outperforming traditional CS-based methods under off-grid conditions.