In this article, we introduce a novel algorithm for efficient near-field
synthetic aperture radar (SAR) imaging for irregular scanning geometries. With
the emergence of fifth-generation (5G) millimeter-wave (mmWave) devices,
near-field SAR imaging is no longer confined to laboratory environments. Recent
advances in positioning technology have attracted significant interest for a
diverse set of new applications in mmWave imaging. However, many use cases,
such as automotive-mounted SAR imaging, unmanned aerial vehicle (UAV) imaging,
and freehand imaging with smartphones, are constrained to irregular scanning
geometries. Whereas traditional near-field SAR imaging systems and quick
personnel security (QPS) scanners employ highly precise motion controllers to
create ideal synthetic arrays, emerging applications, mentioned previously,
inherently cannot achieve such ideal positioning. In addition, many Internet of
Things (IoT) and 5G applications impose strict size and computational
complexity limitations that must be considered for edge mmWave imaging
technology. In this study, we propose a novel algorithm to leverage the
advantages of non-cooperative SAR scanning patterns, small form-factor
multiple-input multiple-output (MIMO) radars, and efficient monostatic planar
image reconstruction algorithms. We propose a framework to mathematically
decompose arbitrary and irregular sampling geometries and a joint solution to
mitigate multistatic array imaging artifacts. The proposed algorithm is
validated through simulations and an empirical study of arbitrary scanning
scenarios. Our algorithm achieves high-resolution and high-efficiency
near-field MIMO-SAR imaging, and is an elegant solution to computationally
constrained irregularly sampled imaging problems.

提出了一种新颖的算法，用于有效地处理不规则扫描几何的近场合成孔径雷达 (SAR) 成像，并利用非协作 SAR 扫描模式，小型 MIMO 雷达和高效单靶平面图像重建算法实现了高分辨率和高效率的近场 MIMO-SAR 成像。