Computationally Efficient Capon- and APES-based Coherence Spectrum Estimation
Angelopoulos, Kostas ; Glentis, George-Othan and Jakobsson, Andreas LU
(2012)
In IEEE Transactions on Signal Processing
60(12).
p.6674-6681
- Abstract
- The coherence spectrum is of notable interest as a bivariate spectral measure in a variety of application, and the topic has lately attracted interest with the recent formulation of several ligh-resolution data adaptive estimators. In this work, we further this development with the presentation of computationally efficient implementations of the Caponand APE S-based MSC estimators. The presented implementations furthers the recent development of exploiting the estimators’ inherently low displacement rank of the necessary products of Toeplitz-like matrices to include also the required cross-correlation covariance matrices for the mentioned
coherence algorithms. Numerical simulations together with theoretical
complexity... (More) - The coherence spectrum is of notable interest as a bivariate spectral measure in a variety of application, and the topic has lately attracted interest with the recent formulation of several ligh-resolution data adaptive estimators. In this work, we further this development with the presentation of computationally efficient implementations of the Caponand APE S-based MSC estimators. The presented implementations furthers the recent development of exploiting the estimators’ inherently low displacement rank of the necessary products of Toeplitz-like matrices to include also the required cross-correlation covariance matrices for the mentioned
coherence algorithms. Numerical simulations together with theoretical
complexity measures illustrate the performance of the proposed implementations. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3193673
- author
- Angelopoulos, Kostas
; Glentis, George-Othan
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Coherence spectrum, data adaptive estimators, efficient algorithms
- in
- IEEE Transactions on Signal Processing
- volume
- 60
- issue
- 12
- pages
- 6674 - 6681
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000311805000043
- scopus:84870508915
- ISSN
- 1053-587X
- DOI
- 10.1109/TSP.2012.2217612
- language
- English
- LU publication?
- yes
- id
- 9ef88a71-9e79-4dfd-8c03-28d77bb85bdb (old id 3193673)
- date added to LUP
- 2016-04-01 10:50:54
- date last changed
- 2022-03-27 20:04:10
@article{9ef88a71-9e79-4dfd-8c03-28d77bb85bdb,
abstract = {{The coherence spectrum is of notable interest as a bivariate spectral measure in a variety of application, and the topic has lately attracted interest with the recent formulation of several ligh-resolution data adaptive estimators. In this work, we further this development with the presentation of computationally efficient implementations of the Caponand APE S-based MSC estimators. The presented implementations furthers the recent development of exploiting the estimators’ inherently low displacement rank of the necessary products of Toeplitz-like matrices to include also the required cross-correlation covariance matrices for the mentioned<br/><br>
coherence algorithms. Numerical simulations together with theoretical<br/><br>
complexity measures illustrate the performance of the proposed implementations.}},
author = {{Angelopoulos, Kostas and Glentis, George-Othan and Jakobsson, Andreas}},
issn = {{1053-587X}},
keywords = {{Coherence spectrum; data adaptive estimators; efficient algorithms}},
language = {{eng}},
number = {{12}},
pages = {{6674--6681}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Signal Processing}},
title = {{Computationally Efficient Capon- and APES-based Coherence Spectrum Estimation}},
url = {{https://lup.lub.lu.se/search/files/2181507/3993815.pdf}},
doi = {{10.1109/TSP.2012.2217612}},
volume = {{60}},
year = {{2012}},
}