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Maximum total generalized correntropy adaptive filtering for parameter estimation
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DOI：10.1016/j.sigpro.2022.108787.png)
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In this study, we consider the parameter estimation problem for an errors-in-variables (EIV) model with impulse noise. New adaptive filtering, called the maximum total generalized correntropy (MTGC) adap-tive filtering algorithm, is developed to further improve the robustness of conventional adaptive filtering algorithms. Specifically, the proposed approach is derived by integrating the generalized maximum cor-rentropy (GMC) criterion into the total least square (TLS) framework. The local stability and steady-state properties are investigated with the aid of the generalized Gaussian process. Numerical simulations are presented to illustrate the effectiveness of the proposed method in the presence of impulse noise.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Generalized maximum correntropy criterion
Errors -in -variables
Maximum total generalized correntropy
Generalized Gaussian process
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
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Maximum mixture total correntropy adaptive filtering against impulsive noises.png)
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