RIS-Aided Cooperative ISAC Network for Imaging-Based Low-Altitude Surveillance

2026-01-22

AI Summary

This work proposes a cooperative integrated sensing and communication (ISAC) network assisted by active reconfigurable intelligent surfaces (RIS) to address the challenges of high deployment costs, weak signal strength, and insufficient sensing performance in conventional low-altitude surveillance systems, which hinder the efficient development of the low-altitude economy. For the first time, active RIS is introduced into low-altitude monitoring, enabling a compressive-sensing-based imaging framework that leverages subspace tracking algorithms for target imaging and detection. This approach effectively circumvents error propagation and data association issues inherent in traditional methods. Theoretical analysis and simulation results demonstrate that, under identical power constraints, the proposed scheme significantly outperforms passive RIS-based approaches, achieving high-precision target imaging and detection within an altitude of 300 meters.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Active Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Security and Privacy: Large-scale security measurements

Abstract

The low-altitude economy is integral to the advancement of numerous sectors, necessitating the development of advanced low-altitude surveillance techniques. Nevertheless, conventional methods encounter limitations of high deployment costs and low signal strength. This study proposes a reconfigurable intelligent surface (RIS)-aided cooperative integrated sensing and communication (ISAC) network for low-altitude surveillance. This network employs RISs to reflect ISAC signals into low-altitude space for sensing. To enhance signal strength, we employ active RIS (ARIS) to amplify the signals. Moreover, in order to avoid error propagation and data association in traditional sensing methods, we model low-altitude surveillance as an imaging problem based on compressed sensing theory, which can be solved through the subspace pursuit algorithm. We derive the Cramer-Rao lower bound (CRLB) of the proposed RIS-aided low-altitude imaging system and analyze the impacts of various system parameters on sensing performance, providing guidance for ISAC system configuration. Numerical results show that ARIS outperforms passive RIS under identical power constraints, achieving effective imaging and target detection at altitudes up to 300 meters.

Problem

• low-altitude surveillance
• high deployment costs
• low signal strength
• RIS-aided ISAC
• imaging-based sensing

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Integrated Sensing and Communication (ISAC)
• Compressed Sensing
• Active RIS
• Low-Altitude Surveillance