Normalized recursive least adaptive threshold nonlinear errors algorithm

This paper proposes a new adaptation algorithm named Normalized Recursive Least Adaptive Threshold Nonlinear Errors (NRLATNE) algorithm for complex-domain adaptive filters which makes the filters fast convergent for correlated filter inputs and robust against two types of impulse noise: one is found in additive observation noise and another at filter input. Analysis of the proposed NRLATNE algorithm is fully developed to theoretically calculate filter convergence behavior. Through experiments with some examples, we demonstrate the effectiveness of the proposed algorithm in improving the filter performance. Good agreement is observed between simulated and theoretically calculated filter convergence that shows the validity of the analysis.