A novel normalized sign algorithm for system identification under impulsive noise interference

TL;DR

This paper introduces a new normalized sign algorithm (NSA) with a convex combination approach, enhancing system identification performance under impulsive noise by balancing convergence speed and steady-state error.

Contribution

The paper proposes a novel NSA-NSA algorithm with a sign-based mixing parameter update and a weight transfer scheme, improving robustness and convergence in impulsive noise environments.

Findings

The proposed algorithm achieves faster convergence.

It maintains low steady-state error.

Theoretical analysis confirms improved robustness.

Abstract

To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies.