Abstract

The recursive least-squares (RLS) algorithm is recognized as a powerful tool in adaptive filtering applications, being characterized by a fast convergence rate, even for highly correlated input signals. Its overall performance is mainly influenced by the forgetting factor, which is a memory-related parameter that is influenced by the filter length. On the other hand, its robustness (in noisy conditions) can be controlled by using an appropriate regularization term. In order to achieve a proper balance between the main performance criteria, a regularized RLS algorithm with variable (i.e., time-dependent) forgetting factor is designed in this paper. The proposed solution is tested in the framework of echo cancellation, being supported by several simulation results obtained in different noisy scenarios.