UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems
Abstract
Unmanned aerial vehicle-enabled synthetic aperture radar (SAR) offers significant potential for providing communication services to ground users while sensing the surrounding environment in low-altitude integrated sensing and communication (ISAC) systems. However, its practical deployment is challenged by known non-ULM trajectories and unknown position errors, which violate conventional SAR assumptions and cause severe image defocusing. We address these non-ideal impacts by formulating the ISAC SAR imaging as an exact linear inverse problem to accurately model arbitrary trajectories with multiple-input multiple-output orthogonal frequency division multiplexing waveforms. Based on this formulation, we develop a diffusion posterior sampling (DPS)-based super-resolution SAR framework, where a pre-trained diffusion model provides a data-driven image prior and the exact physical model enforces measurement consistency during posterior inference. The inference process is further accelerated for practical implementation. For unknown position errors, we develop a joint iterative DPS and motion compensation (MoCo) optimization algorithm with discrete cosine transform reparameterization and coarse-to-fine spectral scheduling. Simulations demonstrate that our framework achieves high-accuracy reconstruction across diverse trajectories and remarkable super-resolution, maintaining greater than 21.3 dB peak signal-to-noise ratio (PSNR) even under half physical resolution truncation. Under unknown motion errors, the joint DPS and MoCo algorithm achieves 23.65 dB PSNR with a residual position-error estimation error of 0.19 cm.
Related
- Kinetic Langevin Meets Split Gibbs: Accelerated Posterior Sampling for Imaging Inverse Problems with Diffusion Priors
- Accelerating posterior sampling in Bayesian inverse problems with diffusion models: an online-offline perspective
- Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding
- Mixed Poisson diffusion models and application to posterior sampling
- A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
BibTeX
@misc{zhu2026imaging,
title = {{UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems}},
author = {Keying Zhu and Le Liang and Jie Yang and Fan Liu and Shi Jin},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14516},
eprint = {2610.14516},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14516},
}
RIS
TY - UNPB
TI - UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems
AU - Keying Zhu
AU - Le Liang
AU - Jie Yang
AU - Fan Liu
AU - Shi Jin
PY - 2026
DA - 2026/10/10
PB - arXiv
DO - 10.48550/arxiv.2610.14516
UR - https://arxiv.org/abs/2610.14516
AB - Unmanned aerial vehicle-enabled synthetic aperture radar (SAR) offers significant potential for providing communication services to ground users while sensing the surrounding environment in low-altitude integrated sensing and communication (ISAC) systems. However, its practical deployment is challenged by known non-ULM trajectories and unknown position errors, which violate conventional SAR assumptions and cause severe image defocusing. We address these non-ideal impacts by formulating the ISAC SAR imaging as an exact linear inverse problem to accurately model arbitrary trajectories with multiple-input multiple-output orthogonal frequency division multiplexing waveforms. Based on this formulation, we develop a diffusion posterior sampling (DPS)-based super-resolution SAR framework, where a pre-trained diffusion model provides a data-driven image prior and the exact physical model enforces measurement consistency during posterior inference. The inference process is further accelerated for practical implementation. For unknown position errors, we develop a joint iterative DPS and motion compensation (MoCo) optimization algorithm with discrete cosine transform reparameterization and coarse-to-fine spectral scheduling. Simulations demonstrate that our framework achieves high-accuracy reconstruction across diverse trajectories and remarkable super-resolution, maintaining greater than 21.3 dB peak signal-to-noise ratio (PSNR) even under half physical resolution truncation. Under unknown motion errors, the joint DPS and MoCo algorithm achieves 23.65 dB PSNR with a residual position-error estimation error of 0.19 cm.
ER -