Accurate Frequency Estimation with Fewer DFT Interpolations based on Pad Approximation

Wu, K; Zhang, A; Huang, X; Guo, YJ

Accurate Frequency Estimation with Fewer DFT Interpolations based on Pad Approximation

Wu, K

Zhang, A Huang, X

Guo, YJ

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2021, 70, (7), pp. 7267-7271
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Wu, K https://orcid.org/0000-0003-1298-6404
dc.contributor.author	Zhang, A
dc.contributor.author	Huang, X https://orcid.org/0000-0001-6869-5732
dc.contributor.author	Guo, YJ
dc.date.accessioned	2022-01-27T04:59:10Z
dc.date.available	2022-01-27T04:59:10Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2021, 70, (7), pp. 7267-7271
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/153651
dc.description.abstract	Frequency estimation is a fundamental problem in many areas. The well-known A&M and its variant estimators have established an estimation framework by iteratively interpolating the discrete Fourier transform (DFT) coefficients. In general, previous estimators require two DFT interpolations per iteration, have uneven initial estimation performance against frequencies, and are incompetent for small sample numbers due to low-order approximations involved. Exploiting the iterative estimation framework of A&M, we unprecedentedly introduce the Pad Approximation to frequency estimation, unveil some features about the updating function used for refining the estimation in each iteration, and develop a simple closed-form solution to solving the residual estimation error. Extensive simulation results are provided, validating the superiority of the new estimator over the state-the-art estimators in wide ranges of key parameters.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP160102219
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2021.3087869
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.title	Accurate Frequency Estimation with Fewer DFT Interpolations based on Pad Approximation
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-06-09T00:00:00+1000Z
dc.date.updated	2022-01-27T04:59:09Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	7

Abstract:

Frequency estimation is a fundamental problem in many areas. The well-known A&M and its variant estimators have established an estimation framework by iteratively interpolating the discrete Fourier transform (DFT) coefficients. In general, previous estimators require two DFT interpolations per iteration, have uneven initial estimation performance against frequencies, and are incompetent for small sample numbers due to low-order approximations involved. Exploiting the iterative estimation framework of A&M, we unprecedentedly introduce the Pad Approximation to frequency estimation, unveil some features about the updating function used for refining the estimation in each iteration, and develop a simple closed-form solution to solving the residual estimation error. Extensive simulation results are provided, validating the superiority of the new estimator over the state-the-art estimators in wide ranges of key parameters.

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http://hdl.handle.net/10453/153651