Authors:
Madjid Arezki
1
;
Ahmed Benallal
1
;
Abderezak Guessoum
1
and
Daoud Berkani
2
Affiliations:
1
Faculty of Engineering-University of Blida, Algeria
;
2
Signal & Communications Laboratory - (ENP), Algeria
Keyword(s):
Fast RLS, NLMS, FNTF, Adaptive Filtering, Convergence Speed, Tracking capability.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
Abstract:
In this paper, we propose a new algorithm M-SMFTF which reduces the complexity of the simplified FTF-type (SMFTF) algorithm by using a new recursive method to compute the likelihood variable. The computational complexity was reduced from 7L to 6L, where L is the finite impulse response filter length. Furthermore, this computational complexity can be significantly reduced to (2L+4P) when used with a reduced P-size forward predictor. Finally, some simulation results are presented and our algorithm shows an improvement in convergence over the normalized least mean square (NLMS).