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.
Cooperative Bistatic ISAC Systems for Low-Altitude Economy
National Mobile Communications Research Laboratory, Southeast University（国家移动通信重点实验室，东南大学）；China Mobile Group Design Institute Co., Ltd.（中国移动集团设计院有限公司）
RIS-Aided Cooperative ISAC Network for Imaging-Based Low-Altitude Surveillance
Southeast University（东南大学）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信实验室）；Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University（东南大学教育部复杂工程测控重点实验室）；Frontiers Science Center for Mobile Information Communication and Security, Southeast University（东南大学移动信息通信与安全前沿科学中心）
Ray Antenna Array Enhanced Low-Altitude ISAC: Performance Analysis and Beamforming Design
The Sixty-third Research Institute, National University of Defense Technology（国防科技大学第六十三研究所）；National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；Purple Mountain Laboratories（紫金山实验室）
Fusion of Monostatic and Bistatic Sensing for ISAC-Enabled Low-Altitude Environment Mapping
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与工程学院）；Nantong University（南通大学）；School of Transportation and Civil Engineering, Nantong University（南通大学交通与土木工程学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）
Online Material-Labeled Environment Reconstruction via Bayesian Multipath Attribution for Low-Altitude ISAC
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与电子工程学院）；School of Transportation and Civil Engineering, Nantong University（南通大学交通与土木工程学院）；College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子信息工程学院）
UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems
National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；Purple Mountain Laboratories（紫金山实验室）；Frontiers Science Center for Mobile Information Communication and Security, Southeast University（东南大学移动信息通信与安全前沿科学中心）
