Multiple window correlation estimation with applications in adaptive filtering

Christopher F. Mullins, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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 languageEnglish (US)
Title of host publicationConference Record of the 26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992
PublisherIEEE Computer Society
Pages857-860
Number of pages4
ISBN (Electronic)0818631600
DOIs
StatePublished - Jan 1 1992
Externally publishedYes
Event26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992 - Pacific Grove, United States
Duration: Oct 26 1992Oct 28 1992

Publication series

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

Conference

Conference26th Asilomar Conference on Signals, Systems and Computers, ACSSC 1992
CountryUnited States
CityPacific Grove
Period10/26/9210/28/92

Fingerprint Dive into the research topics of 'Multiple window correlation estimation with applications in adaptive filtering'. Together they form a unique fingerprint.

Cite this