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Frequency domain maximum correntropy criterion spline adaptive filtering.

Wenyan Guo1,2, Yongfeng Zhi3,4, Kai Feng5

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Area of Science:

  • Signal Processing
  • Adaptive Filtering
  • Noise Reduction

Background:

  • Frequency domain spline adaptive filters (FDSAF) reduce computational complexity.
  • FDSAF algorithms struggle to suppress non-Gaussian impulsive noises effectively.

Purpose of the Study:

  • Develop a novel adaptive filter to suppress non-Gaussian impulsive noises.
  • Maintain comparable operational time to existing methods.
  • Analyze the learning rate bound for algorithm convergence.

Main Methods:

  • Introduced a maximum correntropy criterion (MCC) into the FDSAF framework.
  • Developed the frequency domain maximum correntropy criterion spline adaptive filter (FDSAF-MCC).
  • Studied the learning rate bound for FDSAF-MCC convergence.

Main Results:

  • FDSAF-MCC demonstrates superior performance in suppressing non-Gaussian impulsive noises.
  • The proposed algorithm achieves comparable operational time.
  • Experimental simulations validate the effectiveness of FDSAF-MCC.

Conclusions:

  • FDSAF-MCC is an effective solution for non-Gaussian impulsive noise suppression.
  • The new algorithm offers enhanced performance compared to traditional FDSAF.
  • The study provides theoretical insights into the algorithm's convergence properties.