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An Approximation of FOCUSS Mean Squared Error

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dc.contributor.author Tausiesakul B.
dc.contributor.author Asavaskulkiet K.
dc.contributor.other Srinakharinwirot University
dc.date.accessioned 2023-11-15T02:08:30Z
dc.date.available 2023-11-15T02:08:30Z
dc.date.issued 2023
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169291656&doi=10.1109%2fJCSSE58229.2023.10202042&partnerID=40&md5=6a5d605cc94472d756f36f3c59d9ef6d
dc.identifier.uri https://ir.swu.ac.th/jspui/handle/123456789/29390
dc.description.abstract FOCal Underdetermined System Solver (FOCUSS) is an estimation method for finding a n unknown vector that potentially has a sparse structure. The application of this estimation technique can be found in several areas, e.g., sparse signal recovery in image reconstruction, wireless communications, etc. The convergence analysis performance and order of convergence of this technique are the focuses of this study. In this work, we investigate its estimation error performance on the second order, in terms of error variance or mean squared error. Since the computation in this algorithm is nonlinear, an exact form of the error performance seems infeasible. Therefore, we derive a closed-form expression that approximates the mean squared error of the FOCUSS. Numerical simulation was conducted to illustrate the closeness of our prediction to the real estimation error. © 2023 IEEE.
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.subject Compressive sensing
dc.subject focal underdetermined system solver
dc.subject mean squared error
dc.title An Approximation of FOCUSS Mean Squared Error
dc.type Conference paper
dc.rights.holder Scopus
dc.identifier.bibliograpycitation Proceedings of JCSSE 2023 - 20th International Joint Conference on Computer Science and Software Engineering. Vol , No. (2023), p.231-236
dc.identifier.doi 10.1109/JCSSE58229.2023.10202042


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