Sparse normalized subband adaptive filter algorithm with \(l_0\)-norm constraint

DOI10.1016/J.JFRANKLIN.2016.09.022zbMath1349.93385OpenAlexW2523264231MaRDI QIDQ344704

Yi Yu, Haiquan Zhao, Badong Chen

Publication date: 24 November 2016

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.09.022

zbMATH Keywords

identificationsparse systemgradient descent principlenormalized subband adaptive filter (NSAF)principle of the minimum perturbation

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Related Items (7)

Variable step size norm-constrained adaptive filtering algorithms ⋮ Block-sparsity regularized maximum correntropy criterion for structured-sparse system identification ⋮ Sparsity-aware normalized subband adaptive filters with jointly optimized parameters ⋮ Robust normalized subband adaptive filter algorithm against impulsive noises and noisy inputs ⋮ Influence of input noises on the mean-square performance of the normalized subband adaptive filter algorithm ⋮ Distributed filtering over wireless sensor networks with parameter and topology uncertainties ⋮ A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method

Cites Work

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• Subband adaptive filtering with \(l_1\)-norm constraint for sparse system identification
• A family of proportionate normalized subband adaptive filter algorithms
• Performance Analysis of $l_0$ Norm Constraint Least Mean Square Algorithm
• Diffusion Sparse Least-Mean Squares Over Networks
• Inherent Decorrelating and Least Perturbation Properties of the Normalized Subband Adaptive Filter
• Generalized Correntropy for Robust<?Pub _newline ?>Adaptive Filtering
• Stochastic Analysis of the Normalized Subband Adaptive Filter Algorithm

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