Lightweight FISTA-Inspired Sparse Reconstruction Network for mmW 3-D Holography

Integrating compressed sensing (CS) with millimeter-wave (mmW) holography has shown great potential to achieve lightweight onboard hardware, low sampling ratio, and high-speed sensing. However, conventional CS-driven algorithms are always limited by nontrivial adjusting of parameters and excessive c...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 20
Hauptverfasser:	Wang, Mou, Wei, Shunjun, Liang, Jiadian, Liu, Shan, Shi, Jun, Zhang, Xiaoling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	3-D holography Algorithms Artificial neural networks Azimuth Coefficients compressed sensing (CS) Computer applications Computer networks Computing costs Deep learning deep unfolding fast iterative shrinkage-thresholding algorithm (FISTA) Holography Image reconstruction Imaging Imaging techniques Iterative methods Kernels Machine learning Millimeter waves millimeter-wave (mmW) imaging Momentum near-field Neural networks Parameters Radar Radar imaging Radar polarimetry Sensors sparse reconstruction Synthetic aperture radar
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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