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：本文提出了一种面向近场合成孔径雷达（SAR）在不规则扫描几何下的新型超分辨率算法。随着第五代（5G）毫米波（mmWave）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及非实验室环境中成为可能。新兴应用如自由手势成像（用户手持雷达在空中扫描）、无人机（UAV）成像及车载SAR面临高分辨率成像的若干独特挑战。首先，恢复SAR图像需获知整个扫描过程中的阵列位置。尽管近年研究引入了基于相机的定位系统以充分估计位置，但高效恢复算法仍是支撑边缘计算与物联网（IoT）技术的关键需求。现有针对非合作近场SAR采样的高效算法虽取得进展，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（ViT）架构，用于处理位置估计误差并在不规则采样几何下实现SAR图像超分辨率（SR）。所提算法Mobile-SRViT是首个采用ViT方法进行SAR图像增强的算法，并通过仿真与实验研究进行了验证。