Improving performance of FxLMS algorithm for active noise control of impulsive noise

This paper concerns active noise control (ANC) of impulsive noise modeled using non-Gaussian stable processes. The most famous filtered-x least mean square (FxLMS) algorithm for ANC systems is based on minimization of variance of error signal. For the impulse noise, the FxLMS algorithm becomes unstable, as second-order moments do not exist for non-Gaussian processes. Among the existing algorithms for ANC of impulsive noise, one is based on minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm. The other is based on modifying, on the basis of statistical properties, the reference signal in update of FxLMS algorithm. In this paper, the proposed algorithm is an extension of the later approach. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithm. It achieves the best performance among the existing algorithms, and at the same computational complexity as that of FxLMS algorithm.