DocumentCode :
1680184
Title :
Incremental combination of RLS and LMS adaptive filters in nonstationary scenarios
Author :
Lopes, Wilder B. ; Lopes, Cassio G.
Author_Institution :
Dept. of Electron. Syst., Univ. of Sao Paulo, Sao Paulo, Brazil
fYear :
2013
Firstpage :
5676
Lastpage :
5680
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.
Keywords :
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; Data models; Least squares approximations; Mathematical model; Signal processing; Steady-state; Stochastic processes; Vectors; Adaptive filtering; convex combination; incremental combination;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
ISSN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2013.6638751
Filename :
6638751
Link To Document :
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