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

Author

Listed:
  • Liheng Bian

    (Beijing Institute of Technology
    Beijing Institute of Technology)

  • Daoyu Li

    (Beijing Institute of Technology)

  • Shuoguang Wang

    (Beijing Institute of Technology
    Key Laboratory of Hebei Province on Unmanned System Intelligent Telemetry & Telecontrol Information 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
    Tangshan Research Institute of Beijing Institute of Technology)

  • Jun Zhang

    (Beijing Institute of Technology)

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.

Suggested Citation

  • Liheng Bian & Daoyu Li & Shuoguang Wang & Chunyang Teng & Jinxuan Wu & Huteng Liu & Hanwen Xu & Xuyang Chang & Guoqiang Zhao & Shiyong Li & Jun Zhang, 2024. "Towards large-scale single-shot millimeter-wave imaging for low-cost security inspection," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50288-y
    DOI: 10.1038/s41467-024-50288-y
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    References listed on IDEAS

    as
    1. Lianlin Li & Hengxin Ruan & Che Liu & Ying Li & Ya Shuang & Andrea Alù & Cheng-Wei Qiu & Tie Jun Cui, 2019. "Machine-learning reprogrammable metasurface imager," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    2. Lianlin Li & Tie Jun Cui & Wei Ji & Shuo Liu & Jun Ding & Xiang Wan & Yun Bo Li & Menghua Jiang & Cheng-Wei Qiu & Shuang Zhang, 2017. "Electromagnetic reprogrammable coding-metasurface holograms," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
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