Adaptive variable step algorithm for missing samples recovery in sparse signals

Recovery of arbitrarily positioned samples that are missing in sparse signals recently attracted significant research interest. Sparse signals with heavily corrupted arbitrary positioned samples could be analysed in the same way as compressive sensed signals by omitting the corrupted samples and con...

Full description

Saved in:
Bibliographic Details
Published in:IET signal processing Vol. 8; no. 3; pp. 246 - 256
Main Authors: Stankovic, Ljubisa, Dakovic, Milos, Vujovic, Stefan
Format: Journal Article
Language:English
Published: Stevenage The Institution of Engineering and Technology 01.05.2014
John Wiley & Sons, Inc
Subjects:
Adaptive algorithms
adaptive variable step algorithm
Algorithms
approximately sparse signals
arbitrarily positioned samples
compressed sensing
compressive sensed signals
corrupted samples
Criteria
Linear programming
missing samples recovery
noisy sparse signals
nondifferentiable forms
Reconstruction
reconstruction problem
Recovery
Signal processing
signal reconstruction
Special Issue on Compressive Sensing and Robust Transforms
standard linear programming form
compressed sensing
compressive sensed signals
adaptive variable step algorithm
arbitrarily positioned samples
standard linear programming form
linear programming
reconstruction problem
corrupted samples
nondifferentiable forms
missing samples recovery
approximately sparse signals
noisy sparse signals
signal reconstruction
ISSN:1751-9675, 1751-9683, 1751-9683
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recovery of arbitrarily positioned samples that are missing in sparse signals recently attracted significant research interest. Sparse signals with heavily corrupted arbitrary positioned samples could be analysed in the same way as compressive sensed signals by omitting the corrupted samples and considering them as unavailable during the recovery process. The reconstruction of the missing samples is done by using one of the well-known reconstruction algorithms. In this study, the authors will propose a very simple and efficient algorithm, applied directly to the concentration measures, without reformulating the reconstruction problem within the standard linear programming form. Direct application of the gradient approach to the non-differentiable forms of measures lead us to introduce a variable step size algorithm. A criterion for changing the adaptive algorithm parameters is presented. The results are illustrated on the examples with sparse signals, including approximately sparse signals and noisy sparse signals.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2013.0385