Adaptive FIR filtering based on minimum L∞-norm

The authors present an efficient adaptive transversal filtering algorithm that is based on the minimum L_∞-norm method. One of the significant contributions of the algorithm is that it provides less computational complexity than the competing normalized least mean square (NLMS) algorithm, yet retains the same motivation as the NLMS algorithm. The performance of this approach, however, is slightly worse than that in the mean-squared sense. It is shown how this algorithm is formulated by the minimum L_∞-norm criterion in the hyperplane. Under the assumption that signals involved are zero-mean and Gaussian, and further employing the independence assumption, the authors then derive a set of nonlinear difference equations that characterize the mean and mean-squared behavior of the filter coefficients