Combining tracking and regularization in recursive least squares identification

The combination of tracking and regularization in recursive identification is studied. It is shown that regularization of the information matrix corresponds to a normalization of the covariance matrix, and that several of the proposed methods for dealing with covariance matrix blow up can be interpreted as approximate implementations of covariance matrix normalization.