Low-complexity data-reuse RLS algorithm with increased robustness features

Abstract

Recent advancements in the field of adaptive filters based on the least-squares minimization criterion propose a stable and efficient recursive least-squares (RLS) algorithm with attractive numerical properties and performance similar to the classical RLS methods. The combination between the RLS and the dichotomous coordinate descent (DCD) iterations (i.e., the RLS-DCD) offers an attractive option for practical applications with low requirements in terms of chip area. This paper employs a data-reuse methodology to further improve the tracking speed of the RLS-DCD algorithm. Furthermore, a regularization principle is used to develop its robustness features in low signal-to-noise working conditions.