A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification
1
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
3
Department of Electronics, Valahia University of Targoviste, 130082 Targoviste, Romania
*
Author to whom correspondence should be addressed.
Symmetry 2017, 9(10), 229; https://doi.org/10.3390/sym9100229
Received: 18 September 2017 / Revised: 1 October 2017 / Accepted: 6 October 2017 / Published: 15 October 2017
Abstract
A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within the framework of the sparse system identifications. The newly-developed GZA-PNMCC algorithm is carried out by introducing a parameter adjusting function into the cost function of the typical proportionate normalized maximum correntropy criterion (PNMCC) to create a zero attraction term. The developed optimization framework unifies the derivation of the zero attraction-based PNMCC algorithms. The developed GZA-PNMCC algorithm further exploits the impulsive response sparsity in comparison with the proportionate-type-based NMCC algorithm due to the GZA zero attraction. The superior performance of the GZA-PNMCC algorithm for estimating a sparse system in a non-Gaussian noise environment is proven by simulations. View Full-TextKeywords:
adaptive filter; normalized maximum correntropy criterion; normalized least mean square (NLMS); proportionate NLMS (PNLMS); zero attracting; impulsive noise
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Li, Y.; Wang, Y.; Albu, F.; Jiang, J. A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification. Symmetry 2017, 9, 229.
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