UAV SAR imaging improves resolution despite motion errors
UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems
Information Theory
Summary
Synthetic aperture radar (SAR) on drones can both communicate and create detailed images of the environment, but drone movements can blur the images. The authors developed a method that models drone paths accurately and uses a special kind of AI (diffusion posterior sampling) to sharpen images even when drone motion is unpredictable. Their approach also corrects unknown position errors to improve image clarity. Simulations show their method produces very clear images better than previous techniques.
What this means in practice
- •For drone system engineers: Implement improved SAR imaging on drones to produce higher resolution maps despite complex flight paths and position errors.
- •For telecommunication network operators: Enhance integrated sensing and communication by maintaining high-quality radar imaging while supporting low-altitude UAV communication services.
Authors
Keying Zhu, Le Liang, Jie Yang, Fan Liu, Shi Jin
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.