2010
Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio
Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing Proceedings Article
En: 2010 2nd International Workshop on Cognitive Information Processing, pp. 382–387, IEEE, Elba, 2010, ISBN: 978-1-4244-6459-3.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient
@inproceedings{Vinuelas-Peris2010,
title = {Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing},
author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5604103},
isbn = {978-1-4244-6459-3},
year = {2010},
date = {2010-01-01},
booktitle = {2010 2nd International Workshop on Cognitive Information Processing},
pages = {382--387},
publisher = {IEEE},
address = {Elba},
abstract = {In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.},
keywords = {Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.