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Coherence Spectrum Estimation From Nonuniformly Sampled Sequences

Butt, Naveed LU and Jakobsson, Andreas LU (2010) In IEEE Signal Processing Letters 17(4). p.339-342
Abstract
Magnitude squared coherence (MSC) is a useful bivariate spectral measure that finds application in a wide variety of fields. In this paper, we develop a nonparametric Capon-based MSC estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed estimator, termed segmented-IAA-MSC (or SIAA-MSC, for short), allows for unevenly sampled data as well as for sequences with arbitrarily missing samples. The estimator first uses segmented-IAA to find accurate estimates of the auto-and cross-covariance matrices of the given sequences. These estimates are then used in a Capon-based MSC estimator reformulated to allow for nonuniformly... (More)
Magnitude squared coherence (MSC) is a useful bivariate spectral measure that finds application in a wide variety of fields. In this paper, we develop a nonparametric Capon-based MSC estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed estimator, termed segmented-IAA-MSC (or SIAA-MSC, for short), allows for unevenly sampled data as well as for sequences with arbitrarily missing samples. The estimator first uses segmented-IAA to find accurate estimates of the auto-and cross-covariance matrices of the given sequences. These estimates are then used in a Capon-based MSC estimator reformulated to allow for nonuniformly sampled sequences. To achieve higher statistical accuracy, the estimation problem is formulated so as to allow for overlapped segmentation of the available data. The proposed SIAA-MSC estimator is found to yield improved estimates as compared to the more commonly used least squares Fourier transform (LSFT) based MSC estimator. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
missing data, spectral analysis, iterative adaptive approach, Capon estimator, coherence spectrum
in
IEEE Signal Processing Letters
volume
17
issue
4
pages
339 - 342
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000274395100002
  • scopus:78650988211
ISSN
1070-9908
DOI
10.1109/LSP.2010.2040227
language
English
LU publication?
yes
id
9ea87589-656d-453f-816c-d4e7fc653e9f (old id 1570755)
date added to LUP
2010-03-16 15:37:15
date last changed
2018-05-29 11:22:00
@article{9ea87589-656d-453f-816c-d4e7fc653e9f,
  abstract     = {Magnitude squared coherence (MSC) is a useful bivariate spectral measure that finds application in a wide variety of fields. In this paper, we develop a nonparametric Capon-based MSC estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed estimator, termed segmented-IAA-MSC (or SIAA-MSC, for short), allows for unevenly sampled data as well as for sequences with arbitrarily missing samples. The estimator first uses segmented-IAA to find accurate estimates of the auto-and cross-covariance matrices of the given sequences. These estimates are then used in a Capon-based MSC estimator reformulated to allow for nonuniformly sampled sequences. To achieve higher statistical accuracy, the estimation problem is formulated so as to allow for overlapped segmentation of the available data. The proposed SIAA-MSC estimator is found to yield improved estimates as compared to the more commonly used least squares Fourier transform (LSFT) based MSC estimator.},
  author       = {Butt, Naveed and Jakobsson, Andreas},
  issn         = {1070-9908},
  keyword      = {missing data,spectral analysis,iterative adaptive approach,Capon estimator,coherence spectrum},
  language     = {eng},
  number       = {4},
  pages        = {339--342},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Signal Processing Letters},
  title        = {Coherence Spectrum Estimation From Nonuniformly Sampled Sequences},
  url          = {http://dx.doi.org/10.1109/LSP.2010.2040227},
  volume       = {17},
  year         = {2010},
}