Correntropy-Based Data Selective Adaptive Filtering
Journal
IEEE Transactions on Circuits and Systems I: Regular Papers
Date Issued
2023-01-01
Author(s)
Abstract
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.
Subjects
Adaptive filters | AWGN | Data selection | Filtering | Filtering algorithms | impulsive noise | Kernel | least mean square (LMS) | maximum correntropy criterion (MCC) | process innovation | resource efficiency | Sensors | Technological innovation
SDGs
Type
journal article