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dc.contributor.authorDülek, Berkanen_US
dc.contributor.authorGezici, Sinanen_US
dc.coverage.spatialÇeşme, Turkeyen_US
dc.date.accessioned2019-07-02T10:35:15Z
dc.date.available2019-07-02T10:35:15Z
dc.date.issued2012-06en_US
dc.identifier.urihttp://hdl.handle.net/11693/52094
dc.descriptionDate of Conference: 17-20 June 2012en_US
dc.description.abstractThe well-known problem of estimating an unknown deterministic parameter vector over a linear system subject to additive Gaussian noise is studied from the perspective of minimizing total sensor measurement cost under a constraint on the log volume of the estimation error confidence ellipsoid. A convex optimization problem is formulated for the general case, and a closed form solution is provided when the system matrix is invertible. Furthermore, effects of system matrix uncertainty are discussed by employing a specific but nevertheless practical uncertainty model. Numerical examples are presented to discuss the theoretical results in detail.en_US
dc.language.isoEnglishen_US
dc.source.title13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE 2012en_US
dc.relation.isversionofhttps://doi.org/10.1109/SPAWC.2012.6292923en_US
dc.subjectWireless sensor networksen_US
dc.subjectParameter estimationen_US
dc.subjectGaussian noiseen_US
dc.subjectMeasurement costen_US
dc.titleA confidence ellipsoid approach for measurement cost minimization under Gaussian noiseen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage339en_US
dc.citation.epage343en_US
dc.identifier.doi10.1109/SPAWC.2012.6292923en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorGezici, Sinan


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