Multiple window correlation estimation with applications in adaptive filtering

Christopher F. Mullins; Georgios B. Giannakis

doi:10.1109/ACSSC.1992.269151

Multiple window correlation estimation with applications in adaptive filtering

Christopher F. Mullins, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A novel autocorrelation estimator is developed using Slepian sequences as multiple windows, which has more degrees of freedom than any singlewindow estimate, including the sample average, with the same frequency domain resolution. Because the Slepian sequences are orthogonal, confidence intervals can be estimated by jacknifing as well as by standard χ²methods. The proposed multiple window estimator is applied to batch AR parameter estimation and recursive least squares equalization. Both applications show significant improvement, especially for small data lengths, while only linearly (in the number of windows) increasing computational complexity. Generalizations to higher-order correlation estimators are delineated.

Original language	English (US)
Title of host publication	Conference Record of the 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992
Publisher	IEEE Computer Society
Pages	857-860
Number of pages	4
ISBN (Electronic)	0818631600
DOIs	https://doi.org/10.1109/ACSSC.1992.269151
State	Published - Jan 1 1992
Externally published	Yes
Event	26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992 - Pacific Grove, United States Duration: Oct 26 1992 → Oct 28 1992

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992
Country/Territory	United States
City	Pacific Grove
Period	10/26/92 → 10/28/92

Publisher link

10.1109/ACSSC.1992.269151

Find It

Find It at U of M Libraries

Cite this

Mullins, C. F., & Giannakis, G. B. (1992). Multiple window correlation estimation with applications in adaptive filtering. In Conference Record of the 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992 (pp. 857-860). [269151] (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.1992.269151

Multiple window correlation estimation with applications in adaptive filtering. / Mullins, Christopher F.; Giannakis, Georgios B.
Conference Record of the 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992. IEEE Computer Society, 1992. p. 857-860 269151 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mullins, CF & Giannakis, GB 1992, Multiple window correlation estimation with applications in adaptive filtering. in Conference Record of the 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992., 269151, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 857-860, 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992, Pacific Grove, United States, 10/26/92. https://doi.org/10.1109/ACSSC.1992.269151

@inproceedings{79ca598c40de4a91878e4323080b7b6d,

title = "Multiple window correlation estimation with applications in adaptive filtering",

abstract = "A novel autocorrelation estimator is developed using Slepian sequences as multiple windows, which has more degrees of freedom than any singlewindow estimate, including the sample average, with the same frequency domain resolution. Because the Slepian sequences are orthogonal, confidence intervals can be estimated by jacknifing as well as by standard χ2methods. The proposed multiple window estimator is applied to batch AR parameter estimation and recursive least squares equalization. Both applications show significant improvement, especially for small data lengths, while only linearly (in the number of windows) increasing computational complexity. Generalizations to higher-order correlation estimators are delineated.",

author = "Mullins, {Christopher F.} and Giannakis, {Georgios B.}",

year = "1992",

month = jan,

day = "1",

doi = "10.1109/ACSSC.1992.269151",

language = "English (US)",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "857--860",

booktitle = "Conference Record of the 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992",

note = "26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992 ; Conference date: 26-10-1992 Through 28-10-1992",

}

TY - GEN

T1 - Multiple window correlation estimation with applications in adaptive filtering

AU - Mullins, Christopher F.

AU - Giannakis, Georgios B.

PY - 1992/1/1

Y1 - 1992/1/1

N2 - A novel autocorrelation estimator is developed using Slepian sequences as multiple windows, which has more degrees of freedom than any singlewindow estimate, including the sample average, with the same frequency domain resolution. Because the Slepian sequences are orthogonal, confidence intervals can be estimated by jacknifing as well as by standard χ2methods. The proposed multiple window estimator is applied to batch AR parameter estimation and recursive least squares equalization. Both applications show significant improvement, especially for small data lengths, while only linearly (in the number of windows) increasing computational complexity. Generalizations to higher-order correlation estimators are delineated.

AB - A novel autocorrelation estimator is developed using Slepian sequences as multiple windows, which has more degrees of freedom than any singlewindow estimate, including the sample average, with the same frequency domain resolution. Because the Slepian sequences are orthogonal, confidence intervals can be estimated by jacknifing as well as by standard χ2methods. The proposed multiple window estimator is applied to batch AR parameter estimation and recursive least squares equalization. Both applications show significant improvement, especially for small data lengths, while only linearly (in the number of windows) increasing computational complexity. Generalizations to higher-order correlation estimators are delineated.

UR - http://www.scopus.com/inward/record.url?scp=84885479481&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885479481&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.1992.269151

DO - 10.1109/ACSSC.1992.269151

M3 - Conference contribution

AN - SCOPUS:84885479481

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 857

EP - 860

BT - Conference Record of the 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992

PB - IEEE Computer Society

T2 - 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992

Y2 - 26 October 1992 through 28 October 1992

ER -

Multiple window correlation estimation with applications in adaptive filtering

Abstract

Publication series

Conference

Publisher link

Find It

Other files and links

Fingerprint

Cite this