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...

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Veröffentlicht in:	IET signal processing Jg. 8; H. 3; S. 246 - 256
Hauptverfasser:	Stankovic, Ljubisa, Dakovic, Milos, Vujovic, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Stevenage The Institution of Engineering and Technology 01.05.2014 John Wiley & Sons, Inc
Schlagworte:	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-Zugang:	Volltext
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