Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior
Abstract Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced i...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-07-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12134 |
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| _version_ | 1832559628843483136 |
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| author | Qilei Zhang Lei Yu Feng He Yifei Ji |
| author_facet | Qilei Zhang Lei Yu Feng He Yifei Ji |
| author_sort | Qilei Zhang |
| collection | DOAJ |
| description | Abstract Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced into the complex BCS framework by exploiting its better sparse characteristic and flexibility than traditional Laplacian prior. Furthermore, by integrating out the noise variance analytically, the MCMBCS algorithm significantly improves the signal recovery performance than the original CMBCS. More importantly, the authors not only present the iterative algorithm but also develop the sub‐optimal fast implementation method based on the marginal likelihood maximisation, which dramatically reduce the computational complexity. Finally, sufficient numerical simulations validate the better performance of the proposed algorithm in reconstruction accuracy and computational effectiveness than existing work. It is revealed that the proposed algorithm has great potential in the complex‐valued signal processing field. |
| format | Article |
| id | doaj-art-6e0f1c2c363842a4a8684158890defeb |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-6e0f1c2c363842a4a8684158890defeb2025-02-03T01:29:41ZengWileyIET Signal Processing1751-96751751-96832022-07-0116560161410.1049/sil2.12134Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture priorQilei Zhang0Lei Yu1Feng He2Yifei Ji3College of Electronics Science National University of Defense Technology Changsha ChinaCollege of Electronics Science National University of Defense Technology Changsha ChinaCollege of Electronics Science National University of Defense Technology Changsha ChinaCollege of Electronics Science National University of Defense Technology Changsha ChinaAbstract Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced into the complex BCS framework by exploiting its better sparse characteristic and flexibility than traditional Laplacian prior. Furthermore, by integrating out the noise variance analytically, the MCMBCS algorithm significantly improves the signal recovery performance than the original CMBCS. More importantly, the authors not only present the iterative algorithm but also develop the sub‐optimal fast implementation method based on the marginal likelihood maximisation, which dramatically reduce the computational complexity. Finally, sufficient numerical simulations validate the better performance of the proposed algorithm in reconstruction accuracy and computational effectiveness than existing work. It is revealed that the proposed algorithm has great potential in the complex‐valued signal processing field.https://doi.org/10.1049/sil2.12134Bayes methodscompressed sensingsignal reconstruction |
| spellingShingle | Qilei Zhang Lei Yu Feng He Yifei Ji Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior IET Signal Processing Bayes methods compressed sensing signal reconstruction |
| title | Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior |
| title_full | Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior |
| title_fullStr | Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior |
| title_full_unstemmed | Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior |
| title_short | Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior |
| title_sort | modified complex multitask bayesian compressive sensing using laplacian scale mixture prior |
| topic | Bayes methods compressed sensing signal reconstruction |
| url | https://doi.org/10.1049/sil2.12134 |
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