Robust Andrew's sine estimate adaptive filtering

TL;DR

This paper introduces two robust adaptive filtering algorithms based on Andrew's sine estimator, improving impulsive noise handling and reducing computational complexity in system identification and partial discharge denoising.

Contribution

It proposes novel IWF-ASE and DCD-ASE algorithms that enhance robustness and efficiency in adaptive filtering under impulsive noise conditions.

Findings

Smaller misalignment compared to conventional methods

Improved performance in impulsive noise environments

Enhanced partial discharge denoising

Abstract

The Andrew's sine function is a robust estimator, which has been used in outlier rejection and robust statistics. However, the performance of such estimator does not receive attention in the field of adaptive filtering techniques. Two Andrew's sine estimator (ASE)-based robust adaptive filtering algorithms are proposed in this brief. Specifically, to achieve improved performance and reduced computational complexity, the iterative Wiener filter (IWF) is an attractive choice. A novel IWF based on ASE (IWF-ASE) is proposed for impulsive noises. To further reduce the computational complexity, the leading dichotomous coordinate descent (DCD) algorithm is combined with the ASE, developing DCD-ASE algorithm. Simulations on system identification demonstrate that the proposed algorithms can achieve smaller misalignment as compared to the conventional IWF, recursive maximum correntropy criterion (RMCC), and DCD-RMCC algorithms in impulsive noise. Furthermore, the proposed algorithms exhibit improved performance in partial discharge (PD) denoising.