Normalized lattice algorithms for least-squares FIR system identification

Recently developed algorithms for least-squares identification of autoregressive models are extended in this paper so as to facilitate least-squares identification of finite impulse-response models. The algorithms belong to the class of square-root normalized lattice algorithms, hence they share the computational efficiency and good numerical behavior of the latter. Two versions are presented-one for identifying time-invariant models and the other for tracking time-varying parameters. New lattice-form realizations of the identified FIR models are given. The general framework is then specialized to the important cases of prediction and smoothing.