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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/99635 http://hdl.handle.net/10220/17417 |
| _version_ | 1811678149121933312 |
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| author | Zhao, Lifan Bi, Guoan Wang, Lu Zhang, Haijian |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Zhao, Lifan Bi, Guoan Wang, Lu Zhang, Haijian |
| author_sort | Zhao, Lifan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:48:40Z |
| format | Journal Article |
| id | ntu-10356/99635 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:48:40Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/996352020-03-07T14:00:31Z An improved auto-calibration algorithm based on sparse Bayesian learning framework Zhao, Lifan Bi, Guoan Wang, Lu Zhang, Haijian School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing 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. 2013-11-07T08:55:06Z 2019-12-06T20:09:44Z 2013-11-07T08:55:06Z 2019-12-06T20:09:44Z 2013 2013 Journal Article 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. https://hdl.handle.net/10356/99635 http://hdl.handle.net/10220/17417 10.1109/LSP.2013.2272462 en IEEE signal processing letters |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Zhao, Lifan Bi, Guoan Wang, Lu Zhang, Haijian An improved auto-calibration algorithm based on sparse Bayesian learning framework |
| title | An improved auto-calibration algorithm based on sparse Bayesian learning framework |
| title_full | An improved auto-calibration algorithm based on sparse Bayesian learning framework |
| title_fullStr | An improved auto-calibration algorithm based on sparse Bayesian learning framework |
| title_full_unstemmed | An improved auto-calibration algorithm based on sparse Bayesian learning framework |
| title_short | An improved auto-calibration algorithm based on sparse Bayesian learning framework |
| title_sort | improved auto calibration algorithm based on sparse bayesian learning framework |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | https://hdl.handle.net/10356/99635 http://hdl.handle.net/10220/17417 |
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