Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation

Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation

The minimum error entropy (MEE) algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise, a normalized version of the MEE algorithm is pro...

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Bibliographic Details
Main Authors: Namyong Kim, Kihyeon Kwon
Format: Article
Language:English
Published: MDPI AG 2016-06-01
Series:Entropy
Subjects:
MEE
step-size
normalization
recursive
power estimation
Online Access:http://www.mdpi.com/1099-4300/18/7/239
Description
Summary:The minimum error entropy (MEE) algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise, a normalized version of the MEE algorithm is proposed. The step size of the MEE algorithm is normalized with the power of input entropy that is estimated recursively for reducing its computational complexity. The proposed algorithm yields lower minimum MSE (mean squared error) and faster convergence speed simultaneously than the original MEE algorithm does in the equalization simulation. On the condition of the same convergence speed, its performance enhancement in steady state MSE is above 3 dB.
ISSN:1099-4300

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