Data-reusing-based filtered-reference adaptive algorithms for active control of impulsive noise sources

Abstract

This paper deals with the adaptive algorithms for active noise control (ANC) systems being employed for the impulsive noise sources. The standard filtered-x least mean square (FxLMS) algorithm; based on the minimization of the variance of the error signal; is well suited for attenuation of Gaussian noise sources. For the impulsive noise; modeled as a stable non-Gaussian process; however, the second order moments do not exist and hence the FxLMS algorithm becomes unstable. The filtered-x least mean p-power (FxLMP) algorithm - based on minimizing the fractional lower order moment (FLOM) - gives robust performance for impulsive ANC; however, its convergence speed is very slow. This paper proposes two data-reusing (DR)-based adaptive algorithms for impulsive ANC. The Proposed-I DR algorithm is based on the normalized step-size FxLMS (NSS-FxLMS) algorithm, and the Proposed-II DR algorithm is based on the Author's recently proposed NSS generalized FxLMP (NSS-GFxLMP) algorithm. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithms in comparison with the existing algorithms.