ML Estimation and Detection of Multiple Frequencies Through Periodogram Estimate Refinement

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/62949
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Title: ML Estimation and Detection of Multiple Frequencies Through Periodogram Estimate Refinement
Authors: Selva, Jesus
Research Group/s: Señales, Sistemas y Telecomunicación
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal
Keywords: Barycentric interpolation | Iterative methods | Maximum likelihood estimation | Multiple frequency estimation
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: Mar-2017
Publisher: IEEE
Citation: IEEE Signal Processing Letters. 2017, 24(3): 249-253. doi:10.1109/LSP.2016.2645283
Abstract: This letter presents a method to detect and estimate multiple frequencies based on the maximum-likelihood principle. The method addresses the three main difficulties in this kind of computation, which are the detection of the number of frequencies, the coarse localization of the cost function's global maximum, and the iterative refinement of an initial estimate. Fundamentally, it consists of first detecting and estimating single frequencies or frequency clusters using the periodogram, and then refining this last estimate through a Newton-type method. This second step is fast because its complexity is independent of the number of samples, once a single fast Fourier transform (FFT) has been computed. These two steps are iteratively repeated until no mode frequency is above a fixed detection threshold. The main advantage of the proposed method is its low complexity, given that its computational burden is just that of a few FFTs in typical scenarios. The method is assessed in a numerical example.
Sponsor: This work was supported by the Spanish Ministry of Economy and Competitiveness and EU FEDER under project TIN2014-55413-C2-2-P.
URI: http://hdl.handle.net/10045/62949
ISSN: 1070-9908 (Print) | 1558-2361 (Online)
DOI: 10.1109/LSP.2016.2645283
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 IEEE
Peer Review: si
Publisher version: http://dx.doi.org/10.1109/LSP.2016.2645283
Appears in Collections:INV - SST - Artículos de Revistas

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