Chandrasekhar adaptive regularizer for adaptive filtering

Adaptivity, stability, fast initial convergence, and low complexity are contradictory exigences in adaptive filtering. The least-mean-squares (LMS) algorithms suffer from a slow initial convergence, and the fast recursive least-squares (RLS) ones present numerical stability problems. In this paper we address this last-mentioned problem and perform a regularization of the initial LS problem by using a priori information about the solution and a finite memory. A new, fast, adaptive, recursive algorithm is presented, based on a state-space representation and Chandrasekhar factorizations.