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dc.contributor.advisorGezici, Sinan
dc.contributor.authorOymak, Berkay
dc.date.accessioned2020-03-06T13:39:53Z
dc.date.available2020-03-06T13:39:53Z
dc.date.copyright2020-01
dc.date.issued2020-03
dc.date.submitted2020-03-06
dc.identifier.urihttp://hdl.handle.net/11693/53513
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2020.en_US
dc.descriptionIncludes bibliographical references (leaves 40-44).en_US
dc.description.abstractWe consider a sensor selection problem for binary hypothesis testing with costconstrained measurements. Random observations related to a parameter vector of interest are assumed to be generated by a linear system corrupted with Gaussian noise. The aim is to decide on the state of the parameter vector based on a set of measurements collected by a limited number of sensors. The cost of each sensor measurement is determined by the number of amplitude levels that can reliably be distinguished. By imposing constraints on the total cost and the maximum number of sensors that can be employed, a sensor selection problem is formulated in order to maximize the detection performance for binary hypothesis testing. By characterizing the form of the solution corresponding to a relaxed version of the optimization problem, a computationally efficient algorithm with near optimal performance is proposed. In addition to the case of fixed sensor measurement costs, we also consider the case where they are subject to design. In particular, the problem of allocating the total cost budget to a limited number of sensors is addressed by designing the measurement accuracy (i.e., the noise variance) of each sensor to be employed in the detection procedure. The optimal solution is obtained in closed form. Numerical examples are presented to corroborate the proposed methods.en_US
dc.description.statementofresponsibilityby Berkay Oymaken_US
dc.format.extentix, 59 leaves ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDetectionen_US
dc.subjectSensor selectionen_US
dc.subjectCost constrainten_US
dc.titleCost constrained sensor selection and design for binary hypothesis testingen_US
dc.title.alternativeİkili hipotez testi için bütçe kısıtlı sensör seçimi ve tasarımıen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB160222
dc.embargo.release2020-09-06


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