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

TL;DR

This work tackles low-altitude surveillance by turning sensing into an imaging problem and leveraging RIS-aided ISAC. It adopts active RIS (ARIS) to boost reflected signals and uses compressed sensing with the Subspace Pursuit algorithm to reconstruct voxel-based scenes from CSI measurements, avoiding expensive beamforming and data association errors. The authors derive a CRLB for CS-based imaging, analyze how TX/RX/RIS placements affect the bound, and demonstrate via simulations that ARIS markedly improves imaging accuracy and detection rates, with usable imaging up to 300 meters altitude. The study provides practical deployment insights, showing RX near the ROI center, TX near an RIS, and RIS spacing that balances path length and aperture, while highlighting energy efficiency benefits of ARIS. These results offer a principled framework for designing RIS-aided ISAC systems in low-altitude environments and guide future optimization of RIS deployments under non-ideal conditions.

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.

Figures (15)

• Figure 1: RIS-aided cooperative ISAC network for imaging-based low-altitude surveillance.
• Figure 2: Comparison between $\mathbb{E} \{ C_n \}$ and $\mathbb{E} \{ \tilde{C}_n \}$.
• Figure 3: $\mathbb{E} \{\tilde{C}_n \}$ versus voxel position when the RX is centered at $\left[0,60\text{m},30\text{m}\right]^{\text{T}}$.
• Figure 4: $\mathbb{E} \{\tilde{C}_n \}$ versus voxel position when the RX is centered at $\left[0,0,30\,\text{m}\right]^{\text{T}}$.
• Figure 5: $\mathbb{E} \{ \bar{\tilde{C} } \}$ versus the RX position.