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Improved regularization for a low-complexity RLS algorithm

The regularization of the recursive least-squares (RLS) adaptive algorithm is a subject rarely approached in literature. The classical regularization method is implemented in the initial stages of the algorithm and has a time-limited effect on the corresponding correlation matrix. This paper proposes a new regularization technique for the RLS — dichotomous coordinate descent (DCD) algorithm, which is applicable for every filter iteration and improves the performance of the algorithm in low signal-to-noise situations. The functionality of the method is demonstrated in acoustic echo cancellation scenarios.