A Fast Robust Adaptive filter using Improved Data-Reuse Method

TL;DR

This paper introduces RTGA-IDROC, a robust adaptive filtering algorithm that combines improved data reuse and online censoring to achieve fast convergence, robustness under noise, and low complexity.

Contribution

It proposes a novel RTGA-IDROC algorithm integrating data reuse and censoring strategies, enhancing convergence speed and robustness in adaptive filtering.

Findings

Effective handling of input noise under errors-in-variables model

Faster convergence in early iterations without sacrificing steady-state performance

Validated superior performance in system identification and acoustic echo cancellation

Abstract

Adaptive filter in complex scenarios demands algorithms that integrate fast convergence, low complexity, and robust performance under diverse noise conditions. To address this challenge, we propose a online censoring robust total generalized adaptive filter using improved data-reused method (RTGA-IDROC) algorithm. The proposed RTGA variant possesses the advantages of both the total least squares (TLS) strategy and the robust generalized adaptive (RGA) function. This algorithm not only effectively handles input noise under the errors-in-variables (EIV) model but also achieves excellent performance across diverse noise environments. Furthermore, to meet the high demand for convergence speed in practical applications, an improved data reuse (IDR) method is introduced, enabling faster convergence in the early stages of iteration without compromising steady-state performance. The increased computational complexity brought by the IDR method is mitigated using the online censoring (OC) strategy. We also modify the OC threshold for real-valued algorithms, as the original threshold was defined for the complex domain. Beyond these algorithmic enhancements, a local stability analysis for the proposed algorithm is provided, and the theoretical steady-state mean-square deviation (MSD) is derived. Finally, simulation experiments in system identification and acoustic echo cancellation (AEC) scenarios validate the superior performance of the proposed algorithm.