Tracking improvements in fast RLS algorithms using a variable forgetting factor

The concept of a variable forgetting factor (VFF) is incorporated into fast recursive least-squares (FRLS) algorithms. Compromises in the data matrix that are needed to do this are examined. Both prewindowed and growing memory covariance algorithms are presented in transversal and lattice structures. Forgetting-factor adaptation schemes, which improve tracking performance over conventional FRLS algorithms, are suggested. Finally, the bias introduced by the use of the VFF is analyzed.>