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
Published in:Entropy (Basel, Switzerland) Switzerland), 2016-07, Vol.18 (7), p.239-239
Main Authors: Kim, Namyong, Kwon, Kihyeon
Format: Article
Language:English
Subjects:
Algorithms
Convergence
Entropy
Equalization
Errors
MEE
Noise
normalization
power estimation
recursive
Robustness
Steady state
step-size
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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
1099-4300
DOI:10.3390/e18070239