An adaptive kernel width convex combination method for maximum correntropy criterion

Autor: Aluisio I. R. Fontes, Leandro L. S. Linhares, João P. F. Guimarães, Luiz F. Q. Silveira, Allan M. Martins
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of the Brazilian Computer Society, Vol 27, Iss 1, Pp 1-13 (2021)
Druh dokumentu: article
ISSN: 0104-6500
1678-4804
DOI: 10.1186/s13173-021-00111-z
Popis: Abstract Recently, the maximum correntropy criterion (MCC) has been successfully applied in numerous applications regarding nonGaussian data processing. MCC employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the adaptive filtering. However, determining the optimal value for such parameter is not always a trivial task. Within this context, this paper proposes a novel method called adaptive convex combination maximum correntropy criterion (ACCMCC), which combines an adaptive kernel algorithm with convex combination techniques. ACCMCC takes advantage from a convex combination of two adaptive MCC-based filters, whose kernel widths are adjusted iteratively as a function of the minimum error value obtained in a predefined estimation window. Results obtained in impulsive noise environment have shown that the proposed approach achieves equivalent convergence rates but with increased accuracy and robustness when compared with other similar algorithms reported in literature.
Databáze: Directory of Open Access Journals
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