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On SNR estimation using IEEE-STD-1057 three-parameter sine wave fit
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-9011-6159
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-2718-0262
KTH, School of Electrical Engineering (EES), Signal Processing.
2013 (English)In: IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2013, IEEE conference proceedings, 2013, 658-661 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, theoretical properties of a maximum-likelihood (ML) estimator of signal-to-noise ratio (SNR) is discussed. The three-parameter sine fit algorithm is employed on a finite and coherently sampled measurement set corrupted by additive white Gaussian noise. Under the Gaussian noise model, the least squares solution provided by the three-parameter sine fit is also ML estimator. Exact distribution and finite sample properties of the SNR estimate are derived. Moreover, an explicit expression for the mean squared error (MSE) of the estimator is given. Simulation results are shown to verify the underlying theoretical results.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 658-661 p.
Keyword [en]
Sine-fit algorithm, maximum-likelihood, coherent sampling, Signal-to-Noise Ratio
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-136145DOI: 10.1109/I2MTC.2013.6555497ISI: 000326900400125Scopus ID: 2-s2.0-84882238770ISBN: 978-1-4673-4621-4 (print)OAI: oai:DiVA.org:kth-136145DiVA: diva2:674255
Conference
IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 6-9 May 2013,Minneapolis, MN
Note

QC 20131216

Available from: 2013-12-03 Created: 2013-12-03 Last updated: 2014-10-07Bibliographically approved

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CiteExportLink to record
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