Title :
The fast Householder filters-RLS adaptive filter
Author_Institution :
Inf. Syst. Lab., Stanford Univ., CA, USA
Abstract :
A fast Householder filter (FHF) QR-RLS algorithm is presented that requires significantly less (by a factor of at least three) computation than previous fast QR-RLS adaptive algorithms. The essential feature of the new method is that it replaces the Givens rotations used in these fast QR algorithms by Householder transformations. A set of filters that characterize the QR factorization of a data matrix is derived, and time updates on this set are determined using a generic Householder updating identity. The FHF requires 7N computations per iteration for the standard prewindowed case, which is the same as the FTF (fast transversal filter) and FAEST fast (non-QR) RLS
Keywords :
adaptive filters; filtering and prediction theory; least squares approximations; QR-RLS algorithm; RLS adaptive filter; fast Householder filter; fast transversal filter; recursive least square; Adaptive algorithm; Adaptive filters; Books; Computer architecture; Filtering algorithms; Information filtering; Information filters; Information systems; Laboratories; Resonance light scattering; Transversal filters;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.115735
