A Robust Complex α-Sigmoid Affine Projection Algorithm Under Non-Gaussian Noise.

Area of Science:

• Signal Processing
• Adaptive Filtering
• Computational Intelligence

Background:

• Traditional adaptive filtering algorithms suffer performance degradation with correlated signals and non-Gaussian noise.
• Impulsive noise and matrix inversion increase computational complexity in existing methods.

Purpose of the Study:

• To propose a robust complex-valued adaptive filtering algorithm for challenging signal environments.
• To reduce computational complexity while maintaining high performance in adaptive systems.

Main Methods:

• Development of a complex-valued affine projection algorithm incorporating an α-Sigmoid cost function (α-CSAP).
• Implicit variable step-size updates via a normalization factor to suppress impulsive noise.
• Theoretical derivation of the steady-state mean square deviation (MSD).

Main Results:

• The α-CSAP algorithm effectively suppresses impulsive noise interference.
• The proposed method avoids matrix inversion, leading to reduced computational complexity.
• Demonstrated superior performance in system identification and beamforming compared to traditional algorithms.

Conclusions:

• The α-CSAP algorithm offers improved robustness and efficiency for complex adaptive filtering.
• This novel approach addresses key limitations of existing adaptive filtering techniques in practical scenarios.