Multiwindow estimators of correlation

Author

McWhorter, L. Todd ; Scharf, L.L.

Author_Institution

Dept. of Electr. & Comput. Eng., Colorado Univ., Boulder, CO, USA

Volume

1

fYear

1994

fDate

31 Oct-2 Nov 1994

Firstpage

14

Abstract

Many algorithms for signal and array processing have embedded within them sample estimates of correlation. In this paper, we prove that the most general symmetric, quadratic, nonnegative-definite, modulation-invariant estimator of correlation is a multiwindow estimator. We establish that multiwindow estimators have the potential to reduce estimator mean-squared error by reducing variance at the expense of controllable bias. When multiwindow estimators are used to solve signal and array processing problems, they have the potential to improve and generalize many standard results

Keywords

array signal processing; correlation methods; estimation theory; spectral analysis; algorithms; array processing; controllable bias; correlation; mean-squared error; modulation-invariant estimator; multiwindow estimators; signal processing; variance; Adaptive filters; Array signal processing; Covariance matrix; Delay estimation; Error correction; Fourier transforms; Signal processing; Signal processing algorithms; Spectral analysis; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-6405-3

Type

conf

DOI

10.1109/ACSSC.1994.471408

Filename

471408