Noise enhanced detection in restricted Neyman-Pearson framework

Simple item page
buir.advisorGezici, Sinan
dc.contributor.authorGültekin, Şan
dc.date.accessioned2016-01-08T18:25:55Z
dc.date.available2016-01-08T18:25:55Z
dc.date.issued2013
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2013.en_US
dc.descriptionIncludes bibliographical references leaves 37-40.en_US
dc.description.abstractHypothesis tests frequently arise in many different engineering problems. Among the most frequently used tests are Bayesian, minimax, and Neyman-Pearson. Even though these tests are capable of addressing many real-life problems, they can be insufficient in certain scenarios. For this reason, developing new hypothesis tests is an important objective. One such developed test is the restricted NeymanPearson test, where one tries to maximize the average detection probability while keeping the worst-case detection and false-alarm probabilities bounded. Finding the best hypothesis testing approach for a problem-at-hand is an important point. Another important one is to employ a detector with an acceptable performance. In particular, if the employed detector is suboptimal, it is crucial that it meets the performance requirements. Previous research has proven that performance of some suboptimal detectors can be improved by adding noise to their inputs, which is known as noise enhancement. In this thesis we investigate noise enhancement according to the restricted Neyman-Pearson framework. To that aim, we formulate an optimization problem for optimal additive noise. Then, generic improvability and nonimprovability conditions are derived, which specify if additive noise can result in performance improvements. We then analyze the special case in which the parameter space is discrete and finite, and show that the optimal noise probability density function is discrete with a certain number of point masses. The improvability results are also extended and more precise conditions are derived. Finally, a numerical example is provided which illustrates the theoretical results and shows the benefits of applying noise enhancement to a suboptimal detector.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:25:55Z (GMT). No. of bitstreams: 1 0006575.pdf: 196715 bytes, checksum: 9490db8e88bb16e5dec0d12d5a975ee9 (MD5)en
dc.description.statementofresponsibilityGültekin, Şanen_US
dc.format.extentix, 40 leaves, graphicsen_US
dc.identifier.urihttp://hdl.handle.net/11693/15874
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDetectionen_US
dc.subjecthypothesis-testingen_US
dc.subjectNeyman-Pearsonen_US
dc.subjectnoise enhanced detectionen_US
dc.subject.lccTK5102.9 .G85 2013en_US
dc.subject.lcshSignal detection.en_US
dc.subject.lcshSignal processing.en_US
dc.subject.lcshElectronic noise.en_US
dc.subject.lcshNoise--Mathematical models.en_US
dc.subject.lcshStatistical hypothesis testing.en_US
dc.titleNoise enhanced detection in restricted Neyman-Pearson frameworken_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical and Electronic Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0006575.pdf
Size:
192.1 KB
Format:
Adobe Portable Document Format
Download

Collections

Graduate School of Engineering and Science