A FAST Algorithm for Adaptive Filtering

In this paper, we propose a new algorithm for adaptive filtering with fast convergence and low complexity. It is the result of a simplified fast transversal filter type algorithm, where the adaptation gain is obtained only from the forward prediction variables and using a new recursive method to compute the likelihood variable. This algorithm presents a certain interest, for the adaptation of very long filters, like those used in the problems of echo acoustic cancellation, due to its reduced complexity, its numerical stability and its convergence in the presence of the speech signal. Its computational complexity is of 6L, where L is the finite impulse response filter length and this is considerably reduced to 2L+4P when we use a reduced P-size (P¿L) forward predictor. Finally, some simulation results are presented and our algorithm shows an improvement in convergence over the normalized least mean square.