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Adaptive step-size iterative algorithm for sparse signal recovery.
- Source :
-
Signal Processing . Nov2018, Vol. 152, p273-285. 13p. - Publication Year :
- 2018
-
Abstract
- Highlights • A joint Bayesian and optimization framework is proposed for sparse signal recovery. • Variational Bayesian (VB) is employed to perform the inference and Euclidean projection (EP) is utilized to impose sparsity. • A maximum likelihood estimator (MLE) of the Bayesian model to speed up the inference with a pre-determined step size is developed. • The convergence of this MLE-EP algorithm is analyzed and compared with the iterative shrinkage/thresholding algorithm. Abstract We develop an efficient algorithm, which can adaptively infer the step-size in each iteration, to recover sparse signal from compressive measurements. This algorithm is formulated as an iteratively alternating projection strategy; the first step projects the measurements/residuals to the signal space, implemented via a Bayesian model, and the second step projects the results obtained in the first step to the ℓ 1 -ball. Variational Bayesian (VB) is employed to perform the inference of the Bayesian model and Euclidean projection (EP) is utilized to impose sparsity; thus our algorithm is dubbed VB-EP. We further derive a maximum likelihood estimator (MLE) of the Bayesian model to speed up the inference with a pre-determined step size. The convergence of this MLE-EP algorithm is analyzed and compared with the iterative shrinkage/thresholding algorithm based on the restricted isometry property of the compressive sensing matrix. Simulation results verify the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 152
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 131795538
- Full Text :
- https://doi.org/10.1016/j.sigpro.2018.06.002