Regularized fast recursive least squares algorithms

Chandrasekhar type factorization is used to develop new fast recursive least squares (FRLS) algorithms for finite memory filtering. Statistical priors are used to get a regularized solution which presents better numerical stability properties than that of the conventional least squares one. The algorithms presented have a unified matrix formulation, and their numerical complexity is related to the factorization rank and then depends on the a priori solution covariance matrix used. Simulation results are presented to illustrate the approach.<>