Two improved normalized subband adaptive filter algorithms with good robustness against impulsive interferences

TL;DR

This paper introduces two modified subband adaptive filter algorithms, MCC-SAF and LC-SAF, that enhance robustness against impulsive interferences while maintaining good convergence and tracking performance.

Contribution

The paper proposes two novel SAF algorithms with individual subband scale functions, improving robustness to impulsive noise and convergence speed over existing methods.

Findings

Robust against impulsive interferences due to adaptive scale functions

Faster convergence rate compared to sign SAF (SSAF)

Similar performance to normalized SAF (NSAF) in interference-free environments

Abstract

To improve the robustness of subband adaptive filter (SAF) against impulsive interferences, we propose two modified SAF algorithms with an individual scale function for each subband, which are derived by maximizing correntropy-based cost function and minimizing logarithm-based cost function, respectively, called MCC-SAF and LC-SAF. Whenever the impulsive interference happens, the subband scale functions can sharply drop the step size, which eliminate the influence of outliers on the tap-weight vector update. Therefore, the proposed algorithms are robust against impulsive interferences, and exhibit the faster convergence rate and better tracking capability than the sign SAF (SSAF) algorithm. Besides, in impulse-free interference environments, the proposed algorithms achieve similar convergence performance as the normalized SAF (NSAF) algorithm. Simulation results have demonstrated the performance of our proposed algorithms.