Paper Info
Title
Incremental combination of RLS and LMS adaptive filters in nonstationary scenarios
Abstract
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 meansquare 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.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6638751
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
adaptive filters,least mean squares methods,recursive filters,LMS adaptive filter,RLS adaptive filter,adaptive rule,highly correlated signal,least mean squares filter,nonstationary scenario,recursive least square filter,tracking analysis,Adaptive filtering,convex combination,incremental combination
Least mean squares filter,Data modeling,Signal processing,Mathematical optimization,Control theory,Convex combination,Computer science,Filter (signal processing),Stochastic process,Adaptive filter,Recursion
Conference
ISSN
Citations 
PageRank 
1520-6149
4
0.44
References 
Authors
7
2
Name
Order
Citations
PageRank
Wilder Bezerra Lopes1222.63
Cássio Guimarães Lopes239432.32