When atomic norm meets the G-filter: A general framework for line spectral estimation

Fuente: arXiv
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Autores principales: Zhu, Bin, Tang, Jiale
Formato: Preprint
Publicado: 2024
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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