Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Area of Science:

• Signal Processing
• Computational Mathematics

Background:

• Existing adaptive filtering algorithms utilize hyperbolic cosine and tangent functions.
• Current algorithms have limitations in parameter settings, accuracy, and convergence performance.
• The hyperbolic sine function has not been explored in adaptive filtering.

Purpose of the Study:

• To propose new adaptive filtering algorithms based on hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF).
• To analyze the computational complexity of the proposed HSF and IHSF algorithms.
• To validate the superiority of the proposed algorithms through simulations.

Main Methods:

• Development of a robust adaptive filtering algorithm utilizing HSF.
• Extension of the HSF algorithm to a novel algorithm based on IHSF.
• Computational complexity analysis and simulation-based validation.

Main Results:

• The proposed HSF and IHSF algorithms demonstrate superior steady-state performance.
• These algorithms exhibit enhanced robustness against impulsive interference compared to existing methods.
• Simulations confirm the superior performance under Gaussian noise and impulsive interference.

Conclusions:

• HSF and IHSF algorithms represent a significant advancement in adaptive filtering.
• The proposed methods offer improved accuracy, convergence, and robustness.
• These algorithms outperform existing adaptive filtering techniques using different hyperbolic functions.