Trimmed-Mean FxLMS Algorithm for ANC in Impulsive Noise

The filtered-x least mean square (FxLMS) algorithm has been widely used for active noise control (ANC), which has a satisfying convergence performance in the Gaussian noise. Nevertheless, the ANC system can receive an unusual signal contaminated by the impulsive noise (IN) in practical applications, which results in serious convergence performance degradation. Some references can be found discussing this issue, but many of the proposed solutions highly rely on the setup of pre-determined parameters depending on special situations. In this paper, the IN problem is dealt with by some robust algorithms, which are adapted from order statistics. With the new FxLMS algorithms, there is no pre-requisite to determine the parameters as required in the past works. Through simulation results, we show that the proposed method has better average noise reduction convergence performance compared to other robust methods.