Square-root covariance ladder algorithms

Square-root normalized ladder algorithms provide an efficient recursive solution to the problem of multichannel autoregressive model fitting. The so-called covariance case is presented here, with emphasis on two special cases, namely the growing memory and sliding memory covariance ladder algorithms. New ladder form realizations for the identified models are presented, leading to convenient methods for computing the model parameters from estimated reflection coefficients. Several application areas of the new algorithms are discussed.