A data version of the Gauss-Markov theorem and its application to adaptive subspace splitting

Author

Scharf, Louis L. ; Thomas, John K.

Author_Institution

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

Volume

3

fYear

1995

fDate

9-12 May 1995

Firstpage

2044

Abstract

How does one adaptively split a measurement subspace into signal and orthogonal subspaces of reduced rank so that detectors, estimators, and quantizers may be adaptively designed from experimental data? The authors provide some answers to this question by decomposing experimental correlations into their Wishart distributed Schur complements and showing how these distributions may be used to identify subspaces

Keywords

Gaussian processes; Markov processes; adaptive estimation; adaptive signal detection; adaptive signal processing; correlation methods; least mean squares methods; quantisation (signal); signal resolution; statistical analysis; Gauss-Markov theorem; Wishart distributed Schur complements; adaptive subspace splitting; data version; decomposition; detectors; estimator; experimental correlations; measurement subspace; orthogonal subspaces; quantizers; signal subspaces; Adaptive filters; Assembly; Covariance matrix; Equations; Estimation error; Gaussian processes; Information filtering; Information filters; Least squares approximation; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.480677

Filename

480677