Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem

In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in which a collection of sparse signal vectors that share a common support are recovered from undersampled noisy measurements. The algorithm, A...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 61; no. 2; pp. 340 - 354
Main Authors:	Ziniel, J., Schniter, P.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.01.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Approximate message passing (AMP) Approximation Approximation algorithms Bayesian methods belief propagation Complexity theory compressed sensing Correlation Detection, estimation, filtering, equalization, prediction Exact sciences and technology expectation-maximization algorithms Inference Information, signal and communications theory joint sparsity Joints Kalman filters Mathematical analysis Mathematical models Message passing multiple measurement vector problem Noise measurement Sampling, quantization Signal and communications theory Signal, noise statistical signal processing Studies Telecommunications and information theory Tuning Vectors Vectors (mathematics) joint sparsity statistical signal processing belief propagation expectation-maximization algorithms Kalman filter Signal estimation Time correlation Computational complexity Approximate message passing (AMP) Credal approach Message passing Signal processing multiple measurement vector problem Automatic tuning Kalman filters EM algorithm Compressed sensing
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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