Petrópolis, Río de Janeiro, Brasil
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National Laboratory for Scientific Computing (LNCC/MCTI)

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Computer Engineer, BE.

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  • Sparsity-aware reuse of coefficients normalised least mean squares

    Electronics Letters

    Noise is an ubiquitous phenomenon that hampers adaptive filtering-based system identification procedures. Recently, the coefficient reuse strategy have been proposed to address the challenging case where the signal-to-noise ratio is low. In this paper, a new derivation approach that incorporate both coefficient reusing (which reduces the oscillation magnitude of each adaptive coefficient) and norm-constrained adaptation (that penalizes non-sparse solutions) is advanced. The solution performs…

    Noise is an ubiquitous phenomenon that hampers adaptive filtering-based system identification procedures. Recently, the coefficient reuse strategy have been proposed to address the challenging case where the signal-to-noise ratio is low. In this paper, a new derivation approach that incorporate both coefficient reusing (which reduces the oscillation magnitude of each adaptive coefficient) and norm-constrained adaptation (that penalizes non-sparse solutions) is advanced. The solution 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 a reduced number of adjustable parameters. The proposed algorithm can be implemented in both normalized and non-normalized versions.

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