In this paper, we introduce an innovative super resolution approach to emerging modes of near-field synthetic aperture radar (SAR) imaging. Recent research extends convolutional neural network (CNN) architectures from the optical to the electromagnetic domain to achieve super resolution on images generated from radar signaling. Specifically, near-field synthetic aperture radar (SAR) imaging, a method for generating high-resolution images by scanning a radar across space to create a synthetic aperture, is of interest due to its high-fidelity spatial sensing capability, low cost devices, and large application space. Since SAR imaging requires large aperture sizes to achieve high resolution, super-resolution algorithms are valuable for many applications. Freehand smartphone SAR, an emerging sensing modality, requires irregular SAR apertures in the near-field and computation on mobile devices. Achieving efficient high-resolution SAR images from irregularly sampled data collected by freehand motion of a smartphone is a challenging task. In this paper, we propose a novel CNN architecture to achieve SAR image super-resolution for mobile applications by employing state-of-the-art SAR processing and deep learning techniques. The proposed algorithm is verified via simulation and an empirical study. Our algorithm demonstrates high-efficiency and high-resolution radar imaging for near-field scenarios with irregular scanning geometries.

翻译：本文针对新兴的近场合成孔径雷达（SAR）成像模式，提出了一种创新的超分辨方法。近年来的研究将卷积神经网络（CNN）架构从光学领域拓展至电磁领域，以实现雷达信号生成图像的超分辨。近场合成孔径雷达（SAR）成像通过空间扫描雷达形成合成孔径来生成高分辨率图像，其高保真空间感知能力、低成本设备及广阔应用前景备受关注。由于SAR成像需要大孔径尺寸才能获得高分辨率，超分辨算法对众多应用具有重要意义。自由手持智能手机SAR作为一种新兴的感知模态，需要在不规则近场SAR孔径上实现移动端计算。从智能手机自由运动采集的非规则采样数据中高效生成高分辨率SAR图像是一项具有挑战性的任务。本文通过融合先进SAR处理技术与深度学习技术，提出一种适用于移动应用的新型CNN架构实现SAR图像超分辨。通过仿真与实验研究验证了所提算法，结果表明该方法在非规则扫描几何构型的近场场景中具有高效高分辨率雷达成像能力。