Tunable line spectral estimators based on state-covariance subspace analysis

Ali Nasiri Amini; Tryphon T. Georgiou

doi:10.1109/TSP.2006.874397

Tunable line spectral estimators based on state-covariance subspace analysis

Ali Nasiri Amini, Tryphon T. Georgiou

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator.

Original language	English (US)
Pages (from-to)	2662-2671
Number of pages	10
Journal	IEEE Transactions on Signal Processing
Volume	54
Issue number	7
DOIs	https://doi.org/10.1109/TSP.2006.874397
State	Published - Jul 2006

Bibliographical note

Funding Information:
Manuscript received May 21, 2004; revised June 15, 2005. This work was supported in part by NSF and AFSOR.The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Steven L. Grant.

Keywords

Harmonic decomposition
Spectral estimation
State-covariance
Subspace methods

Publisher link

10.1109/TSP.2006.874397

Find It

Find It at U of M Libraries

Cite this

@article{8740ba23c6ae4f2db39ec80d1d513d19,

title = "Tunable line spectral estimators based on state-covariance subspace analysis",

abstract = "Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator.",

keywords = "Harmonic decomposition, Spectral estimation, State-covariance, Subspace methods",

author = "{Nasiri Amini}, Ali and Georgiou, {Tryphon T.}",

note = "Funding Information: Manuscript received May 21, 2004; revised June 15, 2005. This work was supported in part by NSF and AFSOR.The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Steven L. Grant.",

year = "2006",

month = jul,

doi = "10.1109/TSP.2006.874397",

language = "English (US)",

volume = "54",

pages = "2662--2671",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "7",

}

TY - JOUR

T1 - Tunable line spectral estimators based on state-covariance subspace analysis

AU - Nasiri Amini, Ali

AU - Georgiou, Tryphon T.

N1 - Funding Information: Manuscript received May 21, 2004; revised June 15, 2005. This work was supported in part by NSF and AFSOR.The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Steven L. Grant.

PY - 2006/7

Y1 - 2006/7

N2 - Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator.

AB - Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator.

KW - Harmonic decomposition

KW - Spectral estimation

KW - State-covariance

KW - Subspace methods

UR - http://www.scopus.com/inward/record.url?scp=33745684757&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745684757&partnerID=8YFLogxK

U2 - 10.1109/TSP.2006.874397

DO - 10.1109/TSP.2006.874397

M3 - Article

AN - SCOPUS:33745684757

SN - 1053-587X

VL - 54

SP - 2662

EP - 2671

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 7

ER -

Tunable line spectral estimators based on state-covariance subspace analysis

Abstract

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this