Statistical analysis of initialization methods for RLS adaptive filters

Abstract

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. >