A fast convergence algorithm for adaptive FIR filters

The authors present a fast convergence algorithm for adaptive FIR (finite impulse response) filters. The algorithm controls the step size on the basis of the moving average of the misadjustment level for a faster convergence. Several operations in addition to the LMS operations are used to estimate the misadjustment and average it to provide a stable step size. The algorithm is applied to adaptive noise cancellation to observe the performance. Computer simulation results for noise cancellation show that the convergence characteristics of the algorithm are improved by as much as 90% over those of the lease-mean-squares (LMS) algorithm and even those of the VS (variable step) algorithm for a multitone signal. For a real speech signal, improvement of the proposed algorithm is degraded, but still more than 50% of the LMS convergence time is saved in the simulated case