DocumentCode :
84369
Title :
New Improved Recursive Least-Squares Adaptive-Filtering Algorithms
Author :
Bhotto, Md Zulfiquar Ali ; Antoniou, Athanasios
Author_Institution :
Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada
Volume :
60
Issue :
6
fYear :
2013
fDate :
Jun-13
Firstpage :
1548
Lastpage :
1558
Abstract :
Two new improved recursive least-squares adaptive-filtering algorithms, one with a variable forgetting factor and the other with a variable convergence factor are proposed. Optimal forgetting and convergence factors are obtained by minimizing the mean square of the noise-free a posteriori error signal. The determination of the optimal forgetting and convergence factors requires information about the noise-free a priori error which is obtained by solving a known L_1-L_2 minimization problem. Simulation results in system-identification and channel-equalization applications are presented which demonstrate that improved steady-state misalignment, tracking capability, and readaptation can be achieved relative to those in some state-of-the-art competing algorithms.
Keywords :
Approximation methods; Convergence; Correlation; Gaussian noise; Minimization; Steady-state; Vectors; Adaptive filters; adaptive-filtering algorithms; convergence factor; forgetting factor; recursive least-squares algorithms;
fLanguage :
English
Journal_Title :
Circuits and Systems I: Regular Papers, IEEE Transactions on
Publisher :
ieee
ISSN :
1549-8328
Type :
jour
DOI :
10.1109/TCSI.2012.2220452
Filename :
6374274
Link To Document :
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