Subspace-based spectrum estimation in innovation models by mixed norm minimization

DOI10.1016/j.jfranklin.2019.01.042zbMath1411.93172OpenAlexW2914276924WikidataQ128459646 ScholiaQ128459646MaRDI QIDQ1738593

Hüseyin Akçay, Semiha Türkay

Publication date: 18 April 2019

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2019.01.042

zbMATH Keywords

discrete-time power spectra of multi-input/multi-output systemssubspace-based spectrum estimation

Mathematics Subject Classification ID

Multivariable systems, multidimensional control systems (93C35) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10)

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
• N2SID: nuclear norm subspace identification of innovation models
• Kernel methods in system identification, machine learning and function estimation: a survey
• Frequency domain subspace-based identification of discrete-time power spectra from uniformly spaced measurements
• Frequency domain subspace-based identification of discrete-time power spectra from nonuniformly spaced measurements
• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
• A reweighted nuclear norm minimization algorithm for low rank matrix recovery
• Nuclear norm system identification with missing inputs and outputs
• Hankel Matrix Rank Minimization with Applications to System Identification and Realization
• Nuclear Norm Spectrum Estimation From Uniformly Spaced Measurements
• Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization
• Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Compressive Wideband Power Spectrum Estimation
• Q-Markov covariance equivalent realization and its application to flexible structure identification
• Subspace-based multivariable system identification from frequency response data
• Spectrum estimation with missing values: A regularized nuclear norm minimization approach