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dc.contributor.authorWong, Lauren Joyen_US
dc.date.accessioned2018-06-13T08:00:50Z
dc.date.available2018-06-13T08:00:50Z
dc.date.issued2018-06-12en_US
dc.identifier.othervt_gsexam:15412en_US
dc.identifier.urihttp://hdl.handle.net/10919/83532
dc.description.abstractSpecific Emitter Identification (SEI) is the association of a received signal to an emitter, and is made possible by the unique and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency (RF) fingerprint. SEI systems are of vital importance to the military for applications such as early warning systems, emitter tracking, and emitter location. More recently, cognitive radio systems have started making use of SEI systems to enforce Dynamic Spectrum Access (DSA) rules. The use of pre-determined and expert defined signal features to characterize the RF fingerprint of emitters of interest limits current state-of-the-art SEI systems in numerous ways. Recent work in RF Machine Learning (RFML) and Convolutional Neural Networks (CNNs) has shown the capability to perform signal processing tasks such as modulation classification, without the need for pre-defined expert features. Given this success, the work presented in this thesis investigates the ability to use CNNs, in place of a traditional expert-defined feature extraction process, to improve upon traditional SEI systems, by developing and analyzing two distinct approaches for performing SEI using CNNs. Neither approach assumes a priori knowledge of the emitters of interest. Further, both approaches use only raw IQ data as input, and are designed to be easily tuned or modified for new operating environments. Results show CNNs can be used to both estimate expert-defined features and to learn emitter-specific features to effectively identify emitters.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectSpecific Emitter Identificationen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectIQ Imbalanceen_US
dc.subjectEstimationen_US
dc.subjectFeature Learningen_US
dc.subjectClusteringen_US
dc.titleOn the Use of Convolutional Neural Networks for Specific Emitter Identificationen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.committeechairMichaels, Alan J.en_US
dc.contributor.committeechairHuang, Jia-Binen_US
dc.contributor.committeememberHeadley, William C.en_US
dc.contributor.committeememberBeex, Aloysius A.en_US


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