Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition

In this paper, the problem of multidimensional single-tone frequency estimation of sinusoids embedded in white Gaussian noise is investigated. By extracting the two-dimensional (2-D) slice matrices from the multidimensional data, we construct a covariance matrix associated with only one dimension, f...

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Main Authors: Hui Cao, Long-Ting Huang, Yuntao Wu, Qi Liu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8719905/
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spelling doaj-298ba1056e344130a172210438e11bf42021-03-29T23:29:29ZengIEEEIEEE Access2169-35362019-01-017681536815910.1109/ACCESS.2019.29181528719905Multidimensional Single-Tone Frequency Estimation Based on QR DecompositionHui Cao0Long-Ting Huang1https://orcid.org/0000-0003-2236-0501Yuntao Wu2https://orcid.org/0000-0001-7911-4104Qi Liu3https://orcid.org/0000-0001-5378-6404School of Information Engineering, Wuhan University of Technology, Wuhan, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan, ChinaSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, ChinaDepartment of Electronic Engineering, City University of Hong Kong, Hong KongIn this paper, the problem of multidimensional single-tone frequency estimation of sinusoids embedded in white Gaussian noise is investigated. By extracting the two-dimensional (2-D) slice matrices from the multidimensional data, we construct a covariance matrix associated with only one dimension, from which the corresponding frequency is estimated with the utilization of a QR decomposition based iterative method. The frequencies of the remaining dimensions are then obtained following similar procedures. Moreover, the mean square error of the estimated frequencies is devised. The computer simulations are also included to evaluate the performance of the proposed method by comparing with the several state-of-the-art algorithms and Cramér-Rao lower bound.https://ieeexplore.ieee.org/document/8719905/Frequency estimationQR factorizationparameter estimation
collection DOAJ
language English
format Article
sources DOAJ
author Hui Cao
Long-Ting Huang
Yuntao Wu
Qi Liu
spellingShingle Hui Cao
Long-Ting Huang
Yuntao Wu
Qi Liu
Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition
IEEE Access
Frequency estimation
QR factorization
parameter estimation
author_facet Hui Cao
Long-Ting Huang
Yuntao Wu
Qi Liu
author_sort Hui Cao
title Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition
title_short Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition
title_full Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition
title_fullStr Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition
title_full_unstemmed Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition
title_sort multidimensional single-tone frequency estimation based on qr decomposition
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description In this paper, the problem of multidimensional single-tone frequency estimation of sinusoids embedded in white Gaussian noise is investigated. By extracting the two-dimensional (2-D) slice matrices from the multidimensional data, we construct a covariance matrix associated with only one dimension, from which the corresponding frequency is estimated with the utilization of a QR decomposition based iterative method. The frequencies of the remaining dimensions are then obtained following similar procedures. Moreover, the mean square error of the estimated frequencies is devised. The computer simulations are also included to evaluate the performance of the proposed method by comparing with the several state-of-the-art algorithms and Cramér-Rao lower bound.
topic Frequency estimation
QR factorization
parameter estimation
url https://ieeexplore.ieee.org/document/8719905/
work_keys_str_mv AT huicao multidimensionalsingletonefrequencyestimationbasedonqrdecomposition
AT longtinghuang multidimensionalsingletonefrequencyestimationbasedonqrdecomposition
AT yuntaowu multidimensionalsingletonefrequencyestimationbasedonqrdecomposition
AT qiliu multidimensionalsingletonefrequencyestimationbasedonqrdecomposition
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