INCREMENTAL COMBINATION OF RLS AND LMS ADAPTIVE FILTERS IN NONSTATIONARY SCENARIOS

The incremental combination of adaptive filters (AFs), recently introduced in the literature, presents intrinsic features capable of improving the overall filtering performance. In this work, the incremental combination is extended to account for AFs with different adaptive rules; when Recursive Least-Squares (RLS) and the Least-Mean-Squares (LMS) filters are employed, it is shown, by tracking analysis and extensive simulations, that the new structure is mean-square universal in terms of the combining parameter, particularly in nonstationary scenarios with highly-correlated signals. The simulations and the analytical model match well, showing that the new algorithm outperforms its parallel-independent counterpart.