Compressed Sensing for THz FMCW Radar 3D Imaging | Zendy

Shanshan Gu | Zendy; Guangrong Xi | Zendy; Lingyu Ge | Zendy; Zhong Yang | Zendy; Yizhi Wang | Zendy; Weina Chen | Zendy; Zhenzhong Yu | Zendy

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Compressed Sensing for THz FMCW Radar 3D Imaging

Author(s) -

Shanshan Gu,

Guangrong Xi,

Lingyu Ge,

Zhong Yang,

Yizhi Wang,

Weina Chen,

Zhenzhong Yu

Publication year - 2021

Publication title -

complexity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.447

H-Index - 61

eISSN - 1099-0526

pISSN - 1076-2787

DOI - 10.1155/2021/5576782

Subject(s) - radar , compressed sensing , terahertz radiation , continuous wave radar , radar imaging , computer science , remote sensing , continuous wave , radar engineering details , pulse doppler radar , artificial intelligence , optics , geology , physics , telecommunications , laser

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%.

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