Compressed Sensing for THz FMCW Radar 3D Imaging
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 propose...
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2021-01-01
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Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2021/5576782 |
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doaj-bffcfea34f0d406d82acea75403719852021-09-06T00:00:33ZengHindawi-WileyComplexity1099-05262021-01-01202110.1155/2021/5576782Compressed Sensing for THz FMCW Radar 3D ImagingShanshan Gu0Guangrong Xi1Lingyu Ge2Zhong Yang3Yizhi Wang4Weina Chen5Zhenzhong Yu6College of Intelligent Science and Control EngineeringShanghai Geometrical Perception and Learning Co., Ltd.Nanjing Metro Group Co., Ltd.College of Intelligent Science and Control EngineeringCollege of Intelligent Science and Control EngineeringCollege of Intelligent Science and Control EngineeringCollege of Intelligent Science and Control EngineeringA 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%.http://dx.doi.org/10.1155/2021/5576782 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shanshan Gu Guangrong Xi Lingyu Ge Zhong Yang Yizhi Wang Weina Chen Zhenzhong Yu |
spellingShingle |
Shanshan Gu Guangrong Xi Lingyu Ge Zhong Yang Yizhi Wang Weina Chen Zhenzhong Yu Compressed Sensing for THz FMCW Radar 3D Imaging Complexity |
author_facet |
Shanshan Gu Guangrong Xi Lingyu Ge Zhong Yang Yizhi Wang Weina Chen Zhenzhong Yu |
author_sort |
Shanshan Gu |
title |
Compressed Sensing for THz FMCW Radar 3D Imaging |
title_short |
Compressed Sensing for THz FMCW Radar 3D Imaging |
title_full |
Compressed Sensing for THz FMCW Radar 3D Imaging |
title_fullStr |
Compressed Sensing for THz FMCW Radar 3D Imaging |
title_full_unstemmed |
Compressed Sensing for THz FMCW Radar 3D Imaging |
title_sort |
compressed sensing for thz fmcw radar 3d imaging |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1099-0526 |
publishDate |
2021-01-01 |
description |
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%. |
url |
http://dx.doi.org/10.1155/2021/5576782 |
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