Towards large-scale single-shot millimeter-wave imaging for low-cost security inspection

Liheng Bian (), Daoyu Li, Shuoguang Wang, Chunyang Teng, Jinxuan Wu, Huteng Liu, Hanwen Xu, Xuyang Chang, Guoqiang Zhao, Shiyong Li () and Jun Zhang ()
Additional contact information
Liheng Bian: Beijing Institute of Technology
Daoyu Li: Beijing Institute of Technology
Shuoguang Wang: Beijing Institute of Technology
Chunyang Teng: Beijing Institute of Technology
Jinxuan Wu: Beijing Institute of Technology
Huteng Liu: Beijing Institute of Technology
Hanwen Xu: Beijing Institute of Technology
Xuyang Chang: Beijing Institute of Technology
Guoqiang Zhao: Beijing Institute of Technology
Shiyong Li: Beijing Institute of Technology
Jun Zhang: Beijing Institute of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Millimeter-Wave (MMW) imaging is a promising technique for contactless security inspection. However, the high cost of requisite large-scale antenna arrays hinders its widespread application in high-throughput scenarios. Here, we report a large-scale single-shot MMW imaging framework, achieving low-cost high-fidelity security inspection. We first analyzed the statistical ranking of each array element through 1934 full-sampled MMW echoes. The highest-ranked elements are preferentially selected based on the ranking, building the experimentally optimal sparse sampling strategy that reduces antenna array cost by one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, realizing robust and accurate MMW 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 such a low sampling ratio. With the 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-50288-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50288-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-50288-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().