When atomic norm meets the G-filter: A general framework for line spectral estimation
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916441821806592 |
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| author | Zhu, Bin Tang, Jiale |
| author_facet | Zhu, Bin Tang, Jiale |
| contents | This paper proposes a novel approach for line spectral estimation which combines Georgiou's filter bank (G-filter) with atomic norm minimization (ANM). A key ingredient is a Carathéodory--Fejér-type decomposition for the covariance matrix of the filter output. The resulting optimization problem can be characterized via semidefinite programming and contains the standard ANM for line spectral estimation as a special case. Simulations show that our approach outperforms the standard ANM in terms of recovering the number of spectral lines when the signal-to-noise ratio is no lower than 0 dB and the G-filter is suitably designed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When atomic norm meets the G-filter: A general framework for line spectral estimation Zhu, Bin Tang, Jiale Signal Processing This paper proposes a novel approach for line spectral estimation which combines Georgiou's filter bank (G-filter) with atomic norm minimization (ANM). A key ingredient is a Carathéodory--Fejér-type decomposition for the covariance matrix of the filter output. The resulting optimization problem can be characterized via semidefinite programming and contains the standard ANM for line spectral estimation as a special case. Simulations show that our approach outperforms the standard ANM in terms of recovering the number of spectral lines when the signal-to-noise ratio is no lower than 0 dB and the G-filter is suitably designed. |
| title | When atomic norm meets the G-filter: A general framework for line spectral estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2410.12349 |