An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework

This letter considers the multiplicative perturbation problem in compressive sensing, which has become an increasingly important issue on obtaining robust performance for practical applications. The problem is formulated in a probabilistic model and an auto-calibration sparse Bayesian learning algor...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 20; no. 9; pp. 889 - 892
Main Authors Lifan Zhao, Guoan Bi, Lu Wang, Haijian Zhang
Format Journal Article
LanguageEnglish
Published IEEE 01.09.2013
Subjects
Auto-calibration
Bayes methods
Compressed sensing
compressive sensing
Gaussian distribution
multiplicative perturbation
Noise
Numerical models
Signal processing algorithms
sparse Bayesian framework
Sparse matrices
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ISSN1070-9908
1558-2361
DOI10.1109/LSP.2013.2272462

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Summary:This letter considers the multiplicative perturbation problem in compressive sensing, which has become an increasingly important issue on obtaining robust performance for practical applications. The problem is formulated in a probabilistic model and an auto-calibration sparse Bayesian learning algorithm is proposed. In this algorithm, signal and perturbation are iteratively estimated to achieve sparsity by leveraging a variational Bayesian expectation maximization technique. Results from numerical experiments have demonstrated that the proposed algorithm has achieved improvements on the accuracy of signal reconstruction.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2013.2272462