Block algorithms for the parametric estimation of signals and systems

Abstract

Existing recursive parameter estimation methods use approximated covariance and gradient matrices which are actually computed as functions of the present parameter vector thetaŝ(t) by the matrices computed as functions of all previous parameter estimates thetaŝ(i) for i ≤ t. By reducing the approximations considerably, modified versions of the recursive identification algorithms are obtained. Considering the local averages of the covariance and the gradient and clubbing conveniently with the block nature of estimators, efficient block versions of these algorithms are obtained.