Block normalised iterative hard thresholding algorithm for compressed sensing

In this letter, the authors propose block normalised iterative hard thresholding (BNIHT) algorithm for the recovery of block sparse signal, in which the non-zero elements are presented in clusters. Based on block restricted isometry property, the sufficient conditions to guarantee the convergence of...

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Published in:Electronics letters Vol. 55; no. 17; pp. 957 - 959
Main Authors: Zhang, Xiaobo, Xu, Wenbo, Lin, Jiaru, Dang, Yifei
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 22.08.2019
Subjects:
block IHT algorithm
block restricted isometry property
block sparse signal
BNIHT algorithm
compressed sensing
iterative hard thresholding algorithm
iterative methods
nonzero elements
required iterations
Signal processing
signal reconstruction
sufficient conditions
iterative hard thresholding algorithm
block sparse signal
nonzero elements
sufficient conditions
iterative methods
block IHT algorithm
compressed sensing
signal reconstruction
BNIHT algorithm
block restricted isometry property
required iterations
ISSN:0013-5194, 1350-911X, 1350-911X
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Abstract In this letter, the authors propose block normalised iterative hard thresholding (BNIHT) algorithm for the recovery of block sparse signal, in which the non-zero elements are presented in clusters. Based on block restricted isometry property, the sufficient conditions to guarantee the convergence of BNIHT are derived. In addition, the number of required iterations is obtained. The simulation experiment shows that BNIHT algorithm is superior to the block IHT (BIHT) algorithm when the step size satisfies $\mu \lt 1$μ<1.
AbstractList In this letter, the authors propose block normalised iterative hard thresholding (BNIHT) algorithm for the recovery of block sparse signal, in which the non-zero elements are presented in clusters. Based on block restricted isometry property, the sufficient conditions to guarantee the convergence of BNIHT are derived. In addition, the number of required iterations is obtained. The simulation experiment shows that BNIHT algorithm is superior to the block IHT (BIHT) algorithm when the step size satisfies $\mu \lt 1$μ<1.
In this letter, the authors propose block normalised iterative hard thresholding (BNIHT) algorithm for the recovery of block sparse signal, in which the non‐zero elements are presented in clusters. Based on block restricted isometry property, the sufficient conditions to guarantee the convergence of BNIHT are derived. In addition, the number of required iterations is obtained. The simulation experiment shows that BNIHT algorithm is superior to the block IHT (BIHT) algorithm when the step size satisfies μ<1.
In this letter, the authors propose block normalised iterative hard thresholding (BNIHT) algorithm for the recovery of block sparse signal, in which the non‐zero elements are presented in clusters. Based on block restricted isometry property, the sufficient conditions to guarantee the convergence of BNIHT are derived. In addition, the number of required iterations is obtained. The simulation experiment shows that BNIHT algorithm is superior to the block IHT (BIHT) algorithm when the step size satisfies .
Author Dang, Yifei
Lin, Jiaru
Zhang, Xiaobo
Xu, Wenbo
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Issue 17
Keywords iterative hard thresholding algorithm
block sparse signal
nonzero elements
sufficient conditions
iterative methods
block IHT algorithm
compressed sensing
signal reconstruction
BNIHT algorithm
block restricted isometry property
required iterations
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Snippet In this letter, the authors propose block normalised iterative hard thresholding (BNIHT) algorithm for the recovery of block sparse signal, in which the...
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SubjectTerms block IHT algorithm
block restricted isometry property
block sparse signal
BNIHT algorithm
compressed sensing
iterative hard thresholding algorithm
iterative methods
nonzero elements
required iterations
Signal processing
signal reconstruction
sufficient conditions
Title Block normalised iterative hard thresholding algorithm for compressed sensing
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