Fast transversal filters with data sequence weighting

A fast O(N) algorithm is presented for the adaptive recursive least-squares design of transversal filters. Data sequence weighting is introduced to allow for arbitrarily time-varying weighting strategies, facilitating the tracking of arbitrary nonstationary phenomena. A novel weighting adaptation mechanism is presented that has various desirable features and optimal performance under certain conditions. The algorithm is derived for the prewindowed given data case, and the underlying principles of the derivation are clearly exposed. Exact and soft-constraint initialization are discussed, and an improved restart procedure is proposed