Refinement of Optimal Interpolation Factor for DFT Interpolated Frequency Estimator

Wu, K; Ni, W; Andrew Zhang, J; Liu, RP; Jay Guo, Y

Refinement of Optimal Interpolation Factor for DFT Interpolated Frequency Estimator

Wu, K Ni, W

Andrew Zhang, J Liu, RP Jay Guo, Y

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Communications Letters, 2020, 24, (4), pp. 782-786
Issue Date:: 2020-04-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (473.48 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Wu, K
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Andrew Zhang, J
dc.contributor.author	Liu, RP
dc.contributor.author	Jay Guo, Y
dc.date.accessioned	2020-06-10T01:20:55Z
dc.date.available	2020-06-10T01:20:55Z
dc.date.issued	2020-04-01
dc.identifier.citation	IEEE Communications Letters, 2020, 24, (4), pp. 782-786
dc.identifier.issn	1089-7798
dc.identifier.issn	1558-2558
dc.identifier.uri	http://hdl.handle.net/10453/141220
dc.description.abstract	© 1997-2012 IEEE. Frequency estimation is a fundamental problem in many areas. The previously proposed q-shift estimator (QSE), which interpolates the discrete Fourier transform (DFT) coefficients by a factor of q, enables the estimation accuracy to approach the Cramér-Rao lower bound (CRLB). However, it becomes less effective when the number of samples is small. In this letter, we provide an in-depth analysis to unveil the impact of q on the convergence of QSE, and derive the bounds of a refined region of q that ensures the convergence of QSE to the CRLB even with a small number of samples. Simulations validate our analysis, showing that the refined interpolation factor is able to reduce the estimation mean squared error of QSE by up to 13.14 dB when the sample number is as small as 8.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	Defence Science and Technology Group of the Department of Defence8327
dc.relation	http://purl.org/au-research/grants/arc/DP160102219
dc.relation.ispartof	IEEE Communications Letters
dc.relation.isbasedon	10.1109/LCOMM.2019.2963871
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Refinement of Optimal Interpolation Factor for DFT Interpolated Frequency Estimator
dc.type	Journal Article
utslib.citation.volume	24
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-06-10T01:20:52Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	4

Abstract:

© 1997-2012 IEEE. Frequency estimation is a fundamental problem in many areas. The previously proposed q-shift estimator (QSE), which interpolates the discrete Fourier transform (DFT) coefficients by a factor of q, enables the estimation accuracy to approach the Cramér-Rao lower bound (CRLB). However, it becomes less effective when the number of samples is small. In this letter, we provide an in-depth analysis to unveil the impact of q on the convergence of QSE, and derive the bounds of a refined region of q that ensures the convergence of QSE to the CRLB even with a small number of samples. Simulations validate our analysis, showing that the refined interpolation factor is able to reduce the estimation mean squared error of QSE by up to 13.14 dB when the sample number is as small as 8.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141220