Distributed adaptive LMF algorithm for sparse parameter estimation in Gaussian mixture noise

A distributed adaptive algorithm for estimation of sparse unknown parameters in the presence of nonGaussian noise is proposed in this paper based on normalized least mean fourth (NLMF) criterion. At the first step, local adaptive NLMF algorithm is modified by zero norm in order to speed up the conve...

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Bibliographic Details
Published in7'th International Symposium on Telecommunications (IST'2014) pp. 1046 - 1049
Main Authors Hajiabadi, Mojtaba, Zamiri-Jafarian, Hossein
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2014
Subjects
Adaptation models
Cooperative estimation
Estimation
Gaussian mixture noise
Least squares approximations
NLMF algorithm
Noise
Signal processing algorithms
sparse parameters
Vectors
zero norm
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Summary:A distributed adaptive algorithm for estimation of sparse unknown parameters in the presence of nonGaussian noise is proposed in this paper based on normalized least mean fourth (NLMF) criterion. At the first step, local adaptive NLMF algorithm is modified by zero norm in order to speed up the convergence rate and also to reduce the steady state error power in sparse conditions. Then, the proposed algorithm is extended for distributed scenario in which more improvement in estimation performance is achieved due to cooperation of local adaptive filters. Simulation results show the superiority of the proposed algorithm in comparison with conventional NLMF algorithms.
DOI:10.1109/ISTEL.2014.7000858