Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery

This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entr...

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Bibliographic Details
Published in:2018 26th European Signal Processing Conference (EUSIPCO) pp. 1965 - 1969
Main Authors: Jiang, Qianru, de Lamare, Rodrigo C., Zakharov, Yuriy, Li, Sheng, He, Xiongxiong
Format: Conference Proceeding
Language:English
Published: EURASIP 01.09.2018
Subjects:
Compressed sensing
Europe
Iterative algorithms
iterative hard thresholding
Matching pursuit algorithms
prior information
probability estimation
Signal processing
Signal processing algorithms
Simulation
sparse recovery
ISSN:2076-1465
Online Access:Get full text
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Summary:This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.
ISSN:2076-1465
DOI:10.23919/EUSIPCO.2018.8553389