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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| Published in | IEEE signal processing letters Vol. 20; no. 9; pp. 889 - 892 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.09.2013
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1070-9908 1558-2361 |
| DOI | 10.1109/LSP.2013.2272462 |
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| 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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| AbstractList | 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. |
| Author | Haijian Zhang Lifan Zhao Guoan Bi Lu Wang |
| Author_xml | – sequence: 1 surname: Lifan Zhao fullname: Lifan Zhao email: zhao0145@e.ntu.edu.sg organization: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore – sequence: 2 surname: Guoan Bi fullname: Guoan Bi email: egbi@ntu.edu.sg organization: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore – sequence: 3 surname: Lu Wang fullname: Lu Wang email: wa0001lu@e.ntu.edu.sg organization: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore – sequence: 4 surname: Haijian Zhang fullname: Haijian Zhang email: zhaijian@ntu.edu.sg organization: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore |
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| Cites_doi | 10.1109/TSP.2012.2201152 10.1109/TIP.2009.2032894 10.1109/ICASSP.2012.6288477 10.1109/JSTSP.2009.2039170 10.1109/LGRS.2011.2158797 10.1109/8.509886 10.1109/TSP.2007.914345 10.1109/TSP.2004.831016 10.1007/978-1-4612-5698-4 10.1109/MSP.2007.914731 10.1109/ACSSC.2011.6190118 10.1162/15324430152748236 10.1109/TSP.2007.894265 10.1109/MSP.2008.929620 |
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| SubjectTerms | Auto-calibration Bayes methods Compressed sensing compressive sensing Gaussian distribution multiplicative perturbation Noise Numerical models Signal processing algorithms sparse Bayesian framework Sparse matrices |
| Title | An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework |
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