The tail-normalized iterative hard thresholding algorithm for compressed sensing

The tail -\ell_{1} minimization algorithm significantly enhances the recovery capability of sparse signals compared to the \ell_{1} minimization algorithm. However, solving the tail \ell_{1} minimization problem requires high computational costs and a considerable amount of time. The normalized iter...

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Bibliographic Details
Published in:Chinese Control Conference pp. 3237 - 3242
Main Authors: Qing, Yu, Li, Dequan, Xiao, Shuyuan
Format: Conference Proceeding
Language:English
Published: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Subjects:
Compressed sensing
Computational efficiency
Iterative algorithms
Minimization
normalized iterative hard thresholding
Sensors
sparse signal recovery
Tail
tail-ℓ1 minimization
ISSN:1934-1768
Online Access:Get full text
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Summary:The tail -\ell_{1} minimization algorithm significantly enhances the recovery capability of sparse signals compared to the \ell_{1} minimization algorithm. However, solving the tail \ell_{1} minimization problem requires high computational costs and a considerable amount of time. The normalized iterative hard thresholding (NIHT) algorithm, an improvement over the traditional IHT algorithm, exhibits good computational efficiency. Inspired by the NIHT algorithm, this paper introduces an enhanced NIHT algorithm, namely the tail-NIHT algorithm. The tail-NIHT algorithm retains the computational speed of the NIHT algorithm, greatly improving the efficiency of solving the tail- \ell_{1} minimization problem. Additionally, the tail-NIHT algorithm enhances the sparse signal recovery capability of the NIHT algorithm. Experimental results demonstrate that this algorithm is a promising approach in compressed sensing.
ISSN:1934-1768
DOI:10.23919/CCC63176.2024.10662198