Title :
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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.480677
