A robust, parallelizable, O(m), a posteriori recursive least squares algorithm for efficient adaptive filtering

This correspondence presents a new recursive least squares (RLS) adaptive a lgorithm. The proposed computational scheme uses a finite window by means o f a lemma for the system matrix inversion that is, for the first time, stat ed and proven here. The new algorithm has excellent tracking capabilities. Moreover, its particular structure allows for stabilization by means of a q uite simple method. Its stabilized version performs very well not only for a white noise input but also for nonstationary inputs as well. It is shown to follow music, speech, environmental noise, etc, with particularly good t racking properties. The new algorithm can be parallelized via a simple tech nique. Its parallel form is very fast when implemented with four processors .