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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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 20
Hlavní autoři:	Wang, Mou, Wei, Shunjun, Liang, Jiadian, Liu, Shan, Shi, Jun, Zhang, Xiaoling
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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