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An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework
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DOI：10.1109/LSP.2013.2272462.png)
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Abstract
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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.
Keywords:
Auto-calibration
compressive sensing
multiplicative perturbation
sparse Bayesian framework
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