Sparse signal recovery in the presence of correlated multiple measurement vectors

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2010, Pages 3986-3989

  Zhang, Zhilin ^a   Rao, Bhaskar D ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Compressive sensing; Multiple measurement vectors; Sparse Bayesian learning; Sparse signal recovery

Indexed keywords

COMPRESSED SENSING; RECOVERY; SIGNAL RECONSTRUCTION;

BAYESIAN LEARNING; COMPRESSIVE SENSING; MULTIPLE MEASUREMENT VECTOR; MULTIPLE MEASUREMENTS; PERFORMANCE; RECOVERY ALGORITHMS; SPARSE BAYESIAN; SPARSE BAYESIAN LEARNING; SPARSE SIGNAL RECOVERIES; TEMPORAL CORRELATIONS;

VECTORS;

EID: 78049372052     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2010.5495780     Document Type: Conference Paper

References (13)

1. S. F. Cotter and B.D. Rao et al., "Sparse solutions to linear inverse problems with multiple measurement vectors," IEEE Trans. Signal Process., vol. 53, no. 7, pp. 2477-2488, 2005.
2. J. Chen and X. Huo, "Theoretical results on sparse representations of multiple-measurement vectors," IEEE Trans. Signal Process., vol. 54, no. 12, pp. 4634-4643, 2006.
3. D. P. Wipf and B. D. Rao, "An empirical Bayesian strategy for solving the simultaneous sparse approximation problem," IEEE Trans. Signal Process., vol. 55, no. 7, 2007.
4. Y. C. Eldar and M. Mishali, "Robust recovery of signals from a structured union of subspaces," IEEE Trans. Inf. Theory, vol. 55, no. 11, pp. 5302-5316, 2009.
5. Y. Jin and B.D. Rao, "Insights into the stable recovery of sparse solutions in overcomplete representations using network information theory," in ICASSP, 2008.
6. M.Stojnic et al., "On the reconstruction of block-sparse signals with an optimal number of measurements," IEEE Trans. Signal Process., vol. 57, no. 8, 2009.
7. Joel A Tropp et al., "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," Signal Processing, vol. 86, pp. 572-588, 2006.
8. D. Baron et al, "Distributed compressive sensing," submitted, available at http://www.dsp.ece.rice.edu/cs, 2009.
9. D.Wipf et al., "Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG," NeuroImage, vol. 49, 2010.
10. R. Zdunek and A. Cichocki, "Improved M-FOCUSS algorithm with overlapping blocks for locally smooth sparse signals," IEEE Trans. Signal Process., vol. 56, no. 10, 2008.
11. N. Vaswani, "Kalman filtered compressed sensing," in ICIP, 2008.
12. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. of Mach. Learn. Res., vol. 1, 2001.
13. D.Wipf and S. Nagarajan, "A unified Bayesian framework for MEG/EEG source imaging," NeuroImage, vol. 44, 2009.