Adaptive filtering of stable processes for active attenuation of impulsive noise

Describes a new class of algorithms for active noise control (ANC) for use in environments in which impulsive noise is present. The well known filtered-X and filtered-U ANC algorithms are designed to minimize the variance of a measured error signal. For impulsive noise, which can be modeled using non-Gaussian stable processes, these standard approaches are not appropriate since the second order moments do not exist. The authors propose a new class of adaptive algorithms for ANC that are based on the minimization of a fractional lower order moment, p<2. By studying the effect of p on the convergence behavior of adaptive algorithms, they observe that superior performance is obtained by choosing p≈α where α<2 is a parameter reflecting the degree of impulsiveness of the noise. Applications of this approach to noise cancellation in a duct are presented