DocumentCode
1506805
Title
Subspace selection for partially adaptive sensor array processing
Author
Goldstein, J.Scott ; Reed, Irving S.
Author_Institution
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Volume
33
Issue
2
fYear
1997
fDate
4/1/1997 12:00:00 AM
Firstpage
539
Lastpage
544
Abstract
This paper introduces a cross-spectral metric for subspace selection and rank reduction in partially adaptive minimum variance array processing. The counter-intuitive result that it is suboptimal to perform rank reduction via the selection of the subspace formed by the principal eigenvectors of the array covariance matrix is demonstrated. A cross-spectral metric is shown to be the optimal criterion for reduced-rank Wiener filtering.
Keywords
Wiener filters; adaptive signal processing; array signal processing; eigenvalues and eigenfunctions; filtering theory; cross-spectral metric; optimal criterion; partially adaptive minimum variance array processing; partially adaptive sensor array processing; principal eigenvectors; rank reduction; reduced-rank Wiener filtering; subspace selection; Adaptive arrays; Array signal processing; Covariance matrix; Laboratories; Sensor arrays; Sensor systems; Signal processing; Space technology; Underwater communication; Vectors; Wiener filter;
fLanguage
English
Journal_Title
Aerospace and Electronic Systems, IEEE Transactions on
Publisher
ieee
ISSN
0018-9251
Type
jour
DOI
10.1109/7.575892
Filename
575892
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