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Title: An improved auto-calibration algorithm based on sparse Bayesian learning framework
Authors: Zhao, Lifan
Bi, Guoan
Wang, Lu
Zhang, Haijian
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2013
Source: Zhao, L., Bi, G., Wang, L., & Zhang, H. (2013). An improved auto-calibration algorithm based on sparse Bayesian learning framework. IEEE signal processing letters, 20(9), 889-892.
Series/Report no.: IEEE signal processing letters
Abstract: 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.
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