In this paper, we develop a novel super-resolution algorithm for near-field synthetic-aperture radar (SAR) under irregular scanning geometries. As fifth-generation (5G) millimeter-wave (mmWave) devices are becoming increasingly affordable and available, high-resolution SAR imaging is feasible for end-user applications and non-laboratory environments. Emerging applications such freehand imaging, wherein a handheld radar is scanned throughout space by a user, 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 non-cooperative near-field SAR sampling have been explored in recent work, but suffer 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 address position estimation error and perform SAR image super-resolution (SR) under irregular sampling geometries. The proposed algorithm, Mobile-SRViT, is the first to employ a ViT approach for SAR image enhancement and is validated in simulation and via empirical studies.

翻译：本文提出了一种针对近场合成孔径雷达在非规则扫描几何下的超分辨率新算法。随着第五代毫米波设备日益普及且成本降低，高分辨率SAR成像在终端用户应用和非实验室环境中已具备可行性。新兴应用如手持成像（用户手持雷达在空间中扫描）、无人机成像和车载SAR系统面临高分辨率成像的多项独特挑战。首先，恢复SAR图像需要了解扫描过程中的阵列位置。虽然近期研究提出了基于相机的定位系统以充分估计位置，但高效算法的实现是支持边缘计算和物联网技术的关键要求。针对非协作近场SAR采样的高效算法虽有探索，但在位置估计误差下会出现图像散焦，且仅能生成中等保真度图像。本文提出一种移动端友好的视觉Transformer架构，用于解决位置估计误差并在非规则采样几何下实现SAR图像超分辨率。所提算法Mobile-SRViT是首个采用ViT方法进行SAR图像增强的方案，并已通过仿真与实验研究验证。