UAV SAR Imaging with 5G NR OFDM Signals in NLOS Environments
Qiuyuan Yang, Cunhua Pan, Ruidong Li, Zhenkun Zhang, Hong Ren, Changhong Wang, Jiangzhou Wang

TL;DR
This paper introduces a cooperative ISAC framework using 5G NR OFDM signals for UAV SAR imaging in NLOS environments, employing advanced compressed sensing techniques for high-precision scatterer localization.
Contribution
It proposes a novel cooperative ISAC approach that leverages OFDM signals and a two-stage CS-SAGE scheme for improved SAR imaging in challenging NLOS conditions.
Findings
Effective detection of weak signals in NLOS environments
High-precision scatterer localization demonstrated through simulations
Framework provides insights for practical system design
Abstract
The integration of sensing and communication (ISAC) has significant potential for future wireless systems, enabling efficient spectrum utilization and novel application scenarios. In this paper, we propose a cooperative ISAC framework for synthetic aperture radar (SAR) imaging by leveraging orthogonal frequency division multiplexing (OFDM) communication signals. We address the challenge of severe imaging degradation in non-line-of-sight (NLOS) environments under the QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa). To detect weak signals and eliminate false points, we develop a two-stage compressed sensing-space alternating generalized expectation maximization (CS-SAGE) scheme for high-precision scatterer localization. In stage I, orthogonal matching pursuit (OMP) is employed for coarse estimation to identify the approximate locations of dominant scatterers. Then, the SAGE…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced SAR Imaging Techniques · Radar Systems and Signal Processing · Sparse and Compressive Sensing Techniques
