DocumentCode :
2263499
Title :
Convergence behaviors of the fast LMM/Newton algorithm with Gaussian inputs and contaminated Gaussian noise
Author :
Chan, S.C. ; Zhou, Y.
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
fYear :
2009
fDate :
24-27 May 2009
Firstpage :
2573
Lastpage :
2576
Abstract :
This paper studies the convergence behaviors of the fast least mean M-estimate/Newton adaptive filtering algorithm proposed in (Y. Zhou et al.,2004), which is based on the fast LMS/Newton principle and the minimization of an M-estimate function using robust statistics for robust filtering in impulsive noise. By using the Price´s theorem and its extension for contaminated Gaussian (CG) noise case, the convergence behaviors of the fast LMM/ Newton algorithm with Gaussian inputs and both Gaussian and CG noises are analyzed. Difference equations describing the mean and mean square behaviors of this algorithm and step size bound for ensuring stability are derived. These analytical results reveal the advantages of the fast LMM/Newton algorithm in combating impulsive noise, and they are in good agreement with computer simulation results.
Keywords :
Gaussian noise; Newton method; adaptive filters; difference equations; filtering theory; impulse noise; least mean squares methods; numerical stability; Gaussian inputs; M-estimate function minimization; Newton adaptive filtering algorithm; contaminated Gaussian noise; convergence behaviors; difference equations; fast LMM algorithm; impulsive noise; least mean M-estimate; mean behaviors; mean square behaviors; robust filtering; robust statistics; stability; step size bound; Adaptive filters; Algorithm design and analysis; Character generation; Convergence; Difference equations; Filtering algorithms; Gaussian noise; Least squares approximation; Noise robustness; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 2009. ISCAS 2009. IEEE International Symposium on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-3827-3
Electronic_ISBN :
978-1-4244-3828-0
Type :
conf
DOI :
10.1109/ISCAS.2009.5118327
Filename :
5118327
Link To Document :
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