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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Veröffentlicht in:	2018 26th European Signal Processing Conference (EUSIPCO) S. 1965 - 1969
Hauptverfasser:	Jiang, Qianru, de Lamare, Rodrigo C., Zakharov, Yuriy, Li, Sheng, He, Xiongxiong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	EURASIP 01.09.2018
Schlagworte:	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-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	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