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dc.contributor.advisorAtkinson, Michael
dc.contributor.authorArdohain, Christopher M.
dc.dateJun-16
dc.date.accessioned2016-08-02T19:34:53Z
dc.date.available2016-08-02T19:34:53Z
dc.date.issued2016-06
dc.identifier.urihttp://hdl.handle.net/10945/49454
dc.description.abstractMore than half of all U.S. casualties in Iraq and Afghanistan were caused by improvised explosive devices (IEDs). Despite the spending of over $75 billion to combat this threat, intelligence analysts still lack efficient tools to conduct IED pattern analysis. This thesis evaluates sinusoidal models for effectiveness in assisting in the identification of IED patterns. We formulate three models to test against IED patterns encountered in Iraq and Afghanistan: the Hawkes point process, the non-linear optimization of a sine function, and discrete Fourier transforms (DFT). Non-linear optimization and DFT models both out-perform a mean inter-arrival model when applied to representative IED patterns. We also applied these models against portions of an Iraq IED dataset using a rolling horizon forecast. Lastly, we test model performance when applied to patterns identified from the Iraq dataset. We conclude that although there is not a silver bullet for IED pattern detection, the use of these models in IED environments has the potential to reduce the amount of time and effort intelligence analysts expend when identifying IED patterns. We recommend incorporating these models into a graphic user interface usable by intelligence analysts responsible for IED pattern recognition.en_US
dc.description.urihttp://archive.org/details/iedpatternrecogn1094549454
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleIED pattern recognition using sinusoidal modelsen_US
dc.typeThesisen_US
dc.contributor.secondreaderMcLemore, Connor
dc.contributor.departmentOperations Researchen_US
dc.subject.authorimprovised explosive deviceen_US
dc.subject.authorIEDen_US
dc.subject.authorHawkes point processen_US
dc.subject.authordiscrete Fourier transformsen_US
dc.subject.authorpattern recognitionen_US
dc.subject.authorrolling horizon forecasten_US
dc.description.serviceMajor, United States Armyen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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