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Compressed Sensing for THz FMCW Radar 3D Imaging

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Listed:
  • Shanshan Gu
  • Guangrong Xi
  • Lingyu Ge
  • Zhong Yang
  • Yizhi Wang
  • Weina Chen
  • Zhenzhong Yu
  • Jing Na

Abstract

A terahertz (THz) frequency-modulated continuous wave (FMCW) imaging radar system is developed for high-resolution 3D imaging recently. Aiming at the problems of long data acquisition periods and large sample sizes for the developed imaging system, an algorithm based on compressed sensing is proposed for THz FMCW radar 3D imaging in this paper. Firstly, the FMCW radar signal model is built, and the conventional range migration algorithm is introduced for THz FMCW radar imaging. Then, compressed sensing is extended for THz FMCW radar 3D imaging, and the Newton smooth L0-norm (NSL0) algorithm is presented for sparse measurement data reconstruction. Both simulation and measurement experiments demonstrate the feasibility of reconstructing THz images from measurements even at the sparsity rate of 20%.

Suggested Citation

  • Shanshan Gu & Guangrong Xi & Lingyu Ge & Zhong Yang & Yizhi Wang & Weina Chen & Zhenzhong Yu & Jing Na, 2021. "Compressed Sensing for THz FMCW Radar 3D Imaging," Complexity, Hindawi, vol. 2021, pages 1-10, August.
    • Handle: RePEc:hin:complx:5576782
    DOI: 10.1155/2021/5576782
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    Cited by:

    1. Gupta, Ruchi & Rüdisüli, Martin & Patel, Martin Kumar & Parra, David, 2022. "Smart power-to-gas deployment strategies informed by spatially explicit cost and value models," Applied Energy, Elsevier, vol. 327(C).

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