DocumentCode :
850247
Title :
A normalized robust mixed-norm adaptive algorithm for system identification
Author :
Papoulis, Eftychios V. ; Stathaki, Tania
Author_Institution :
Commun. & Signal Process. Res. Group, Univ. of London, UK
Volume :
11
Issue :
1
fYear :
2004
Firstpage :
56
Lastpage :
59
Abstract :
A normalized robust mixed-norm (NRMN) algorithm for system identification in the presence of impulsive noise is introduced. The standard robust mixed-norm (RMN) algorithm exhibits slow convergence, requires a stationary operating environment, and employs a constant step-size that needs to be determined a priori. To overcome these limitations, the proposed NRMN algorithm introduces a time-varying learning rate and, thus, no longer requires a stationary environment, a major drawback of the RMN algorithm. The proposed NRMN exhibits increased convergence rate and substantially reduces the steady-state coefficient error, as compared to the least mean square (LMS), normalized LMS (NLMS), least absolute deviation (LAD), and RMN algorithm.
Keywords :
FIR filters; adaptive filters; convergence of numerical methods; filtering theory; identification; impulse noise; FIR adaptive filters; NRMN algorithm; adaptive algorithm; adaptive filtering; convergence rate; impulsive noise; normalized robust mixed-norm algorithm; steady-state coefficient error; system identification; time-varying learning rate; Adaptive algorithm; Adaptive filters; Convergence; Finite impulse response filter; Least squares approximation; Noise robustness; Reactive power; Signal processing algorithms; System identification; Working environment noise;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2003.819353
Filename :
1255924
Link To Document :
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