Adaptive and Self-Tuning SBL with Total Variation Priors for Block-Sparse Signal Recovery
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917953719500800 |
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| author | Djelouat, Hamza Leinonen, Reijo Sillanpää, Mikko J. Rao, Bhaskar D. Juntti, Markku |
| author_facet | Djelouat, Hamza Leinonen, Reijo Sillanpää, Mikko J. Rao, Bhaskar D. Juntti, Markku |
| contents | This letter addresses the problem of estimating block sparse signal with unknown group partitions in a multiple measurement vector (MMV) setup. We propose a Bayesian framework by applying an adaptive total variation (TV) penalty on the hyper-parameter space of the sparse signal. The main contributions are two-fold. 1) We extend the TV penalty beyond the immediate neighbor, thus enabling better capture of the signal structure. 2) A dynamic framework is provided to learn the penalty parameter for regularization. It is based on the statistical dependencies between the entries of tentative blocks, thus eliminating the need for fine-tuning. The superior performance of the proposed method is empirically demonstrated by extensive computer simulations with the state-of-art benchmarks. The proposed solution exhibits both excellent performance and robustness against sparsity model mismatch. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09290 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive and Self-Tuning SBL with Total Variation Priors for Block-Sparse Signal Recovery Djelouat, Hamza Leinonen, Reijo Sillanpää, Mikko J. Rao, Bhaskar D. Juntti, Markku Signal Processing This letter addresses the problem of estimating block sparse signal with unknown group partitions in a multiple measurement vector (MMV) setup. We propose a Bayesian framework by applying an adaptive total variation (TV) penalty on the hyper-parameter space of the sparse signal. The main contributions are two-fold. 1) We extend the TV penalty beyond the immediate neighbor, thus enabling better capture of the signal structure. 2) A dynamic framework is provided to learn the penalty parameter for regularization. It is based on the statistical dependencies between the entries of tentative blocks, thus eliminating the need for fine-tuning. The superior performance of the proposed method is empirically demonstrated by extensive computer simulations with the state-of-art benchmarks. The proposed solution exhibits both excellent performance and robustness against sparsity model mismatch. |
| title | Adaptive and Self-Tuning SBL with Total Variation Priors for Block-Sparse Signal Recovery |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.09290 |