Please use this identifier to cite or link to this item:
|Title:||An improved auto-calibration algorithm based on sparse Bayesian learning framework||Authors:||Zhao, Lifan
|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.||URI:||https://hdl.handle.net/10356/99635
|DOI:||http://dx.doi.org/10.1109/LSP.2013.2272462||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.