A fast convergence normalized least-mean-square type algorithm for adaptive filtering

A new adaptive algorithm with fast convergence and low complexity is presented. By using the calculation structure of the dual Kalman variables of the fast transversal filter algorithm and a simple decorrelating technique for the input signal, we obtain an algorithm that exhibits faster convergence speed and enhanced tracking ability compared with the normalized least-mean-square algorithm with similar computational complexity. Copyright (C) 2013 John Wiley & Sons, Ltd.