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A Parallel Kernelized Data-Reusing Maximum Correntropy Algorithm
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DOI：10.1109/TCSII.2020.2974020.png)
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摘要
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In this brief, a parallel kernel data-reusing maximum correntropy (PKDRMC) algorithm is proposed within the framework of nonlinear adaptive filtering. The PKDRMC algorithm consists of two branches, namely, the data-reusing maximum correntropy algorithm, and its kernelized form to combat the non-Gaussian interferences. Then, a new cost function is put forward based on the two different schemes, and it is investigated via nonlinear channel equalization (NCE). The proposed PKDRMC algorithm is robust against impulsive-noise environments. Simulations in the NCE under impulsive-noise settings perform that the PKDRMC algorithm outperforms the popular kernel adaptive algorithms.
Keyword:
Kernel
Signal processing algorithms
Cost function
Convergence
Circuits and systems
Adaptive filters
Adaptive systems
Data-reusing maximum correntropy algorithm
impulsive noise environments
kernel adaptive algorithms
nonlinear channel equalization
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