Gridless compressive-sensing methods for frequency estimation: Points of tangency and links to basics. Stoica, P., Tangy, G., Yang, Z., & Zachariah, D. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 1831-1835, Sep., 2014. abstract bibtex The gridless compressive-sensing methods form the most recent class of approaches that have been proposed for estimating the frequencies of sinusoidal signals from noisy measurements. In this paper we review these methods with the main goal of providing new insights into the relationships between them and their links to the basic approach of nonlinear least squares (NLS).We show that a convex relaxation of penalized NLS leads to the atomic-norm minimization method. This method in turn can be approximated by a gridless version of the SPICE method, for which the dual problem is shown to be equivalent to the global matched filter method.
@InProceedings{6952666,
author = {P. Stoica and G. Tangy and Z. Yang and D. Zachariah},
booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
title = {Gridless compressive-sensing methods for frequency estimation: Points of tangency and links to basics},
year = {2014},
pages = {1831-1835},
abstract = {The gridless compressive-sensing methods form the most recent class of approaches that have been proposed for estimating the frequencies of sinusoidal signals from noisy measurements. In this paper we review these methods with the main goal of providing new insights into the relationships between them and their links to the basic approach of nonlinear least squares (NLS).We show that a convex relaxation of penalized NLS leads to the atomic-norm minimization method. This method in turn can be approximated by a gridless version of the SPICE method, for which the dual problem is shown to be equivalent to the global matched filter method.},
keywords = {compressed sensing;frequency estimation;least squares approximations;matched filters;gridless compressive-sensing method;sinusoidal signal frequency estimation;noisy measurements;nonlinear least squares;NLS;convex relaxation;atomic-norm minimization method;SPICE method;matched filter method;Frequency estimation;SPICE;Estimation;Vectors;Minimization;Covariance matrices;Educational institutions;frequency estimation;sparse signal processing;covariance estimation},
issn = {2076-1465},
month = {Sep.},
}
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