On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment |
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Authors: | S C Chan Y Zhou |
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Affiliation: | (1) Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;(2) Institute of Acoustics, Chinese Academy of Sciences, 21#, North 4th Ring Road West, Beijing, China |
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Abstract: | This paper studies the convergence performance of the transform domain normalized least mean square (TDNLMS) algorithm with
general nonlinearity and the transform domain normalized least mean M-estimate (TDNLMM) algorithm in Gaussian inputs and additive
Gaussian and impulsive noise environment. The TDNLMM algorithm, which is derived from robust M-estimation, has the advantage
of improved performance over the conventional TDNLMS algorithm in combating impulsive noises. Using Price’s theorem and its
extension, the above algorithms can be treated in a single framework respectively for Gaussian and impulsive noise environments.
Further, by introducing new special integral functions, related expectations can be evaluated so as to obtain decoupled difference
equations which describe the mean and mean square behaviors of the TDNLMS and TDNLMM algorithms. These analytical results
reveal the advantages of the TDNLMM algorithm in impulsive noise environment, and are in good agreement with computer simulation
results. |
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