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Article

Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation

Institute of Signal Processing, Johannes Kepler University, 4040 Linz, Austria
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Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Krzysztof Brzostowski
Signals 2021, 2(2), 189-200; https://doi.org/10.3390/signals2020015
Received: 9 November 2020 / Revised: 8 March 2021 / Accepted: 6 April 2021 / Published: 15 April 2021
(This article belongs to the Special Issue Signal Processing and Time-Frequency Analysis)
The frequency estimation of multiple complex sinusoids in the presence of noise is important for many signal processing applications. As already discussed in the literature, this problem can be reformulated as a sparse representation problem. In this letter, such a formulation is derived and an algorithm based on sparse cyclic coordinate descent (SCCD) for estimating the frequency parameters is proposed. The algorithm adaptively reduces the size of the used frequency grid, which eases the computational burden. Simulation results revealed that the proposed algorithm achieves similar performance to the original formulation and the Root-multiple signal classification (MUSIC) algorithm in terms of the mean square error (MSE), with significantly less complexity. View Full-Text
Keywords: frequency estimation; sparse estimation; coordinate descent algorithms frequency estimation; sparse estimation; coordinate descent algorithms
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MDPI and ACS Style

Garcia Guzman, Y.E.; Lunglmayr, M. Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation. Signals 2021, 2, 189-200. https://doi.org/10.3390/signals2020015

AMA Style

Garcia Guzman YE, Lunglmayr M. Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation. Signals. 2021; 2(2):189-200. https://doi.org/10.3390/signals2020015

Chicago/Turabian Style

Garcia Guzman, Yuneisy E., and Michael Lunglmayr. 2021. "Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation" Signals 2, no. 2: 189-200. https://doi.org/10.3390/signals2020015

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