Adaptive filters based on order statistics

An adaptive filter structure which is based on linear combinations of order statistics is proposed. An efficient method to update the filter coefficients is presented, which is based on the minimal mean-square error criterion and which is similar to the Widrow algorithm for the linear adaptive filters. Another method for coefficient update is presented, which is similar to the recursive least squares (RLS) algorithm and which has faster convergence properties. The proposed-filter can adapt well to a variety of noise probability distributions, including impulsive noise. It also performs well in the case of nonstationary signals and, therefore, it is suitable for image-processing applications.>