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Generalized Maximum Complex Correntropy Augmented Adaptive IIR Filtering.

Haotian Zheng1, Guobing Qian1

  • 1College of Electronic and Information Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing 400715, China.

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Summary
This summary is machine-generated.

This study introduces a novel augmented IIR filter adaptive algorithm using the generalized maximum complex correntropy criterion (GMCCC-AIIR). This new method enhances performance in non-Gaussian noise environments, outperforming existing algorithms.

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Area of Science:

  • Signal Processing
  • Adaptive Filtering
  • Complex-Valued Systems

Background:

  • Augmented Infinite Impulse Response (IIR) filter adaptive algorithms are widely studied for complex-valued signals.
  • Existing algorithms often rely on the mean square error (MSE) criterion, which is suboptimal for non-Gaussian noise.
  • Complex correntropy offers robustness against non-Gaussian noise in adaptive filter design.

Purpose of the Study:

  • To propose a new augmented IIR filter adaptive algorithm.
  • To address the limitations of MSE-based algorithms in non-Gaussian noise conditions.
  • To introduce an algorithm based on the generalized maximum complex correntropy criterion (GMCCC).

Main Methods:

  • Development of a novel augmented IIR filter adaptive algorithm.
  • Utilization of the generalized maximum complex correntropy criterion (GMCCC).
  • Employing the complex generalized Gaussian density function as the kernel function for enhanced robustness.

Main Results:

  • The proposed GMCCC-AIIR algorithm demonstrates superior performance compared to existing methods.
  • Stability analysis determined the bounds for the learning rate.
  • Simulation results validated the algorithm's effectiveness and robustness.

Conclusions:

  • The GMCCC-AIIR algorithm provides a robust solution for adaptive filtering in the presence of non-Gaussian noise.
  • The proposed method offers improved performance over traditional MSE-based approaches.
  • The study contributes a novel criterion for designing advanced adaptive filters.