Subband adaptive filtering with \(l_1\)-norm constraint for sparse system identification

DOI10.1155/2013/601623zbMath1299.94042OpenAlexW2140546810WikidataQ59027187 ScholiaQ59027187MaRDI QIDQ474425

Young-Seok Choi

Publication date: 24 November 2014

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/601623

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Identification in stochastic control theory (93E12) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (6)

Sparse normalized subband adaptive filter algorithm with \(l_0\)-norm constraint ⋮ Variable step size norm-constrained adaptive filtering algorithms ⋮ Sparsity-aware normalized subband adaptive filters with jointly optimized parameters ⋮ A sequential selection normalized subband adaptive filter with variable step-size algorithms ⋮ A new subband adaptive filtering algorithm for sparse system identification with impulsive noise ⋮ A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method

Cites Work

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• Adaptive algorithms for sparse system identification
• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• Extensions of compressed sensing
• Proportionate normalized subband adaptive filter algorithms for sparse system identification
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Adaptive filtering in subbands with critical sampling: analysis, experiments, and application to acoustic echo cancellation
• Inherent Decorrelating and Least Perturbation Properties of the Normalized Subband Adaptive Filter

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