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.

翻译：低空经济是推动众多领域发展的关键组成部分，这要求开发先进的低空监视技术。然而，传统方法面临部署成本高和信号强度低的局限。本研究提出了一种用于低空监视的可重构智能表面（RIS）辅助的协作式集成感知与通信（ISAC）网络。该网络利用RIS将ISAC信号反射至低空区域进行感知。为增强信号强度，我们采用有源RIS（ARIS）对信号进行放大。此外，为避免传统感知方法中的误差传播和数据关联问题，我们将低空监视建模为基于压缩感知理论的成像问题，该问题可通过子空间追踪算法求解。我们推导了所提出的RIS辅助低空成像系统的克拉美-罗下界（CRLB），并分析了各种系统参数对感知性能的影响，为ISAC系统配置提供指导。数值结果表明，在相同功率约束下，ARIS优于无源RIS，可在高达300米的高度实现有效成像与目标检测。