Millimeter-wave (MMW) imaging is emerging as a promising technique for safe
security inspection. It achieves a delicate balance between imaging resolution,
penetrability and human safety, resulting in higher resolution compared to
low-frequency microwave, stronger penetrability compared to visible light, and
stronger safety compared to X ray. Despite of recent advance in the last
decades, the high cost of requisite large-scale antenna array hinders
widespread adoption of MMW imaging in practice. To tackle this challenge, we
report a large-scale single-shot MMW imaging framework using sparse antenna
array, achieving low-cost but high-fidelity security inspection under an
interpretable learning scheme. We first collected extensive full-sampled MMW
echoes to study the statistical ranking of each element in the large-scale
array. These elements are then sampled based on the ranking, building the
experimentally optimal sparse sampling strategy that reduces the cost of
antenna array by up to one order of magnitude. Additionally, we derived an
untrained interpretable learning scheme, which realizes robust and accurate
image reconstruction from sparsely sampled echoes. Last, we developed a neural
network for automatic object detection, and experimentally demonstrated
successful detection of concealed centimeter-sized targets using 10% sparse
array, whereas all the other contemporary approaches failed at the same sample
sampling ratio. The performance of the reported technique presents higher than
50% superiority over the existing MMW imaging schemes on various metrics
including precision, recall, and mAP50. With such strong detection ability and
order-of-magnitude cost reduction, we anticipate that this technique provides a
practical way for large-scale single-shot MMW imaging, and could advocate its
further practical applications.

利用稀疏天线阵列和可解释学习方案构建的大规模单次毫米波成像框架具有比现有毫米波成像方案更高的精确度和准确率，能够成功探测到镶嵌在人体内表面的厘米级目标，并可用于实现低成本的安全检测。

面向低成本安全检测的大规模单次毫米波成像技术

Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost  Security Inspection

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。