Sparsity-aware reuse of coefficients normalised least mean squares

Noise is an ubiquitous phenomenon that hampers adaptive filtering-based system identification procedures. Recently, the coefficient reuse strategy has been proposed to address the challenging case where the signal-to-noise ratio is low. In this Letter, a new derivation approach that incorporates bot...

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Bibliographic Details
Published in:Electronics letters 2019-05, Vol.55 (9), p.561-563
Main Authors: Resende, L.C, Haddad, D.B, Ferreira, G.da.R, Campelo, P.H, Petraglia, M.R
Format: Article
Language:English
Subjects:
adaptive filtering
adaptive filters
coefficient reuse strategy
coefficient reusing
least squares approximations
normalised least mean squares
norm‐constrained adaptation
signal denoising
Signal processing
signal‐to‐noise ratio
sparse matrices
sparsity‐aware
system identification
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Summary:Noise is an ubiquitous phenomenon that hampers adaptive filtering-based system identification procedures. Recently, the coefficient reuse strategy has been proposed to address the challenging case where the signal-to-noise ratio is low. In this Letter, a new derivation approach that incorporates both coefficient reusing (which reduces the oscillation magnitude of each adaptive coefficient) and norm-constrained adaptation (that penalises non-sparse solutions) is advanced. The proposed algorithm performs relaxed projections into hyperplanes of interest in order to obtain the desired robustness and high convergence rate in sparse scenarios with low computation burden and reduced number of adjustable parameters. The resulting method can be implemented in both normalised and non-normalised versions.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2019.0489