  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.
