Mean-square performance of a convex combination of two adaptive filters

By Jerónimo Arenas-García, Aníbal R. Figueiras-Vidal, Ali H. Sayed. IEEE Transactions on Signal Processing, 2006

437 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f7f8c83ce63481ffd0aad0f3a8e06ec42a1f43fc

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In Computational Mechanics

In Signal Processing

Citations in the corpus

Adaptive Networks (2014, methodology): “One could also consider situations where agents transmit processed data, e.g., as happens with useful techniques for combining adaptive filter outputs [57].”
Adaptation, Learning, and Optimization over Networks (2014, methodology): “5, a diagonal covariance matrix, Ru, whose entries are generated randomly from within the interval [1, 10], and a cross-covariance vector, rdu, whose entries are also generated randomly within the range [0, 1].”
Diffusion Strategies for Distributed Kalman Filtering and Smoothing (2010, background): “Convex combinations as in (19) have been considered before in the context of adaptive filtering [16], [22], [30]–[32].”
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis (2008, methodology): “Finally, in Section IX we propose an adaptive variant of the diffusion protocol inspired by recent work in convex combination of adaptive filters [14]–[17]; Section X closes the work and points directions for future extensions.”
Diffusion LMS Strategies for Distributed Estimation (2010, methodology): “An adaptive combination rule along these lines can be motivated based on the analysis results of [19].”
Diffusion recursive least-squares for distributed estimation over adaptive networks (2008, methodology): “Convex combinations of the estimates of adaptive ﬁlters as in (11) have been studied in [12]–[14].”
Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis (2010, methodology): “2: As used in [8], [16], another possible way to ensure the nonnegativity of each weight is to introduce a parameterization of the form , where is any real number.”