Low-Complexity Algorithms for Multichannel Spectral Super-Resolution
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915024293855232 |
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| author | Wu, Xunmeng Yang, Zai Xu, Zongben |
| author_facet | Wu, Xunmeng Yang, Zai Xu, Zongben |
| contents | This paper studies the problem of multichannel spectral super-resolution with either constant amplitude (CA) or not. We propose two optimization problems based on low-rank Hankel-Toeplitz matrix factorization. The two problems effectively leverage the multichannel and CA structures, while also enabling the design of low-complexity gradient descent algorithms for their solutions. Extensive simulations show the superior performance of the proposed algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10938 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low-Complexity Algorithms for Multichannel Spectral Super-Resolution Wu, Xunmeng Yang, Zai Xu, Zongben Optimization and Control This paper studies the problem of multichannel spectral super-resolution with either constant amplitude (CA) or not. We propose two optimization problems based on low-rank Hankel-Toeplitz matrix factorization. The two problems effectively leverage the multichannel and CA structures, while also enabling the design of low-complexity gradient descent algorithms for their solutions. Extensive simulations show the superior performance of the proposed algorithms. |
| title | Low-Complexity Algorithms for Multichannel Spectral Super-Resolution |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2411.10938 |