Statistical analysis of initialization methods for RLS adaptive filters

Theoretical analysis is used to evaluate the mean and second-moment properties of recursive least squares algorithms incorporating the fast exact initialization and soft constrained initialization methods during the initialization period. It is shown that the weight vector mean and covariance produc...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 1991-08, Vol.39 (8), p.1793-1804
Main Authors: Hubing, N.E., Alexander, S.T.
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithm design and analysis
Computational complexity
Constraint theory
Filtering algorithms
Least squares methods
Resonance light scattering
Signal processing algorithms
Statistical analysis
Transversal filters
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Theoretical analysis is used to evaluate the mean and second-moment properties of recursive least squares algorithms incorporating the fast exact initialization and soft constrained initialization methods during the initialization period. It is shown that the weight vector mean and covariance produced by fast exact initialization are undefined for this period. Theoretical results are derived for soft constrained initialization that show that the weight vector mean and covariance are finite, and expressions are given for these quantities. Simulations for various cases are presented to support the accuracy of these theoretical results.< >
ISSN:1053-587X
1941-0476
DOI:10.1109/78.91150