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dc.contributor.authorSuliman, Mohamed Abdalla Elhag
dc.contributor.authorBallal, Tarig
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2019-04-16T11:00:11Z
dc.date.available2017-12-28T07:32:15Z
dc.date.available2019-04-16T11:00:11Z
dc.date.issued2018-05-12
dc.identifier.citationSuliman MA, Ballal T, Al-Naffouri TY (2018) Perturbation-based regularization for signal estimation in linear discrete ill-posed problems. Signal Processing 152: 35–46. Available: http://dx.doi.org/10.1016/j.sigpro.2018.05.005.
dc.identifier.issn0165-1684
dc.identifier.doi10.1016/j.sigpro.2018.05.005
dc.identifier.urihttp://hdl.handle.net/10754/626536
dc.description.abstractEstimating the values of unknown parameters in ill-posed problems from corrupted measured data presents formidable challenges in ill-posed problems. In such problems, many of the fundamental estimation methods fail to provide meaningful stabilized solutions. In this work, we propose a new regularization approach combined with a new regularization-parameter selection method for linear least-squares discrete ill-posed problems called constrained perturbation regularization approach (COPRA). The proposed COPRA is based on perturbing the singular-value structure of the linear model matrix to enhance the stability of the problem solution. Unlike many regularization methods that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator, which is the objective in many estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results show that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach enjoys the shortest runtime and offers the highest level of robustness of all the tested benchmark regularization methods.
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number# 2221 from the Office of Competitive Research and Grant OSR-2016-KKI-2899.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0165168418301658
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in SIGNAL PROCESSING DOI: 10.1016/j.sigpro.2018.05.005. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectLinear estimation
dc.subjectill-posed problems
dc.subjectlinear least squares
dc.subjectregularization
dc.subjectperturbed models
dc.titlePerturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-Posed Problems
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalSignal Processing
dc.eprint.versionPost-print
dc.identifier.arxivid1611.09742
kaust.personSuliman, Mohamed Abdalla Elhag
kaust.personBallal, Tarig
kaust.personAl-Naffouri, Tareq Y.
kaust.grant.numberOSR-2016-KKI-2899.
refterms.dateFOA2018-06-13T11:31:52Z
dc.date.published-online2018-05-12
dc.date.published-print2018-11
dc.date.posted2016-11-29


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NOTICE: this is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in SIGNAL PROCESSING DOI: 10.1016/j.sigpro.2018.05.005. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as NOTICE: this is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in SIGNAL PROCESSING DOI: 10.1016/j.sigpro.2018.05.005. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
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