Correntropy-Based Data Selective Adaptive Filtering

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on square...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2024-02, Vol.71 (2), p.754-766
Main Authors: Chien, Ying-Ren, Wu, Sheng-Teng, Tsao, Hen-Wai, Diniz, Paulo S. R.
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
AWGN
Data collection
Data selection
Filtering algorithms
impulsive noise
Kernel functions
least mean square (LMS)
Least mean squares methods
maximum correntropy criterion (MCC)
process innovation
resource efficiency
Resource management
Sensors
Technological innovation
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Summary:Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2023.3339632