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

A new recursive least squares (RLS) adaptive algorithm is proposed. The suggested computational methodology employs a finite window by means of a lemma for the system matrix inversion that is stated and proven for the first time. The algorithm has excellent tracking capabilities. Also, its particular structure permits stabilization through a simple method. Its stabilized version performs well not only for a white noise input but also for nonstationary inputs as well.