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In this paper, we develop a novel super-resolution algorithm for near-field synthetic-aperture radar (SAR) under array perturbation. As fifth-generation (5G) millimeter-wave (mmWave) devices become increasingly affordable and available, high-resolution SAR imaging is feasible for end-user applications and non-laboratory environments. Emerging applications, such as freehand imaging, unmanned aerial vehicle (UAV) imaging, and automotive SAR, face several unique challenges for high-resolution imaging. First, recovering a SAR image requires knowledge of the array positions throughout the scan. While recent work has introduced camera-based positioning systems capable of adequately estimating the position, recovering the algorithm efficiently is a requirement to enable edge and Internet of Things (IoT) technologies. Efficient algorithms for near-field SAR have been developed; however, they suffer from image defocusing under position estimation error and can only produce medium-fidelity images. In this paper, we introduce a mobile-friend vision transformer (ViT) architecture to perform SAR image super-resolution and artifact mitigation under array position errors. The proposed algorithm, Mobile-SRViT, is the first to employ a ViT approach for SAR image enhancement and is validated through simulation and empirical studies.

A Vision Transformer Approach for Efficient Near-Field SAR Super-Resolution under Array Perturbation