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dc.contributor.advisorSingham, Dashi I.
dc.contributor.authorNunez, Jesse A.
dc.dateMar-17
dc.date.accessioned2017-05-10T16:31:57Z
dc.date.available2017-05-10T16:31:57Z
dc.date.issued2017-03
dc.identifier.urihttp://hdl.handle.net/10945/53026
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractDue to uncertainty in target locations, stochastic models are implemented to provide a representation of location distribution. The reliability of these models has a profound effect on the ability to successfully interdict these targets. A key factor in the reliability of a model is the incorporation of information updates. A common method for incorporating information updates is Kalman filtering. However, given the probable nonlinear and non-Gaussian nature of target movement models, the fidelity of solutions provided by Kalman filtering could be significantly degraded. A more robust methodology needs to be employed. This thesis uses an updating algorithm known as particle filtering to incorporate information updates concerning the target's position. Particle filtering is a nonparametric filtering technique that is adaptable and flexible. The particle filter is incorporated into a model that uses a stochastic process known as a Brownian bridge to model target movement. A Brownian bridge models target movement with minimal information and allows for uncertainty during periods when target location is unknown. As new intelligence arrives, the particle filter is used to update a probabilistic heat map of target position. The main goal of this thesis is to design a stochastic model integrating both the Brownian bridge model and particle filtering.en_US
dc.description.urihttp://archive.org/details/particlefilterin1094553026
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.titleParticle filtering methods for incorporating intelligence updatesen_US
dc.typeThesisen_US
dc.contributor.secondreaderAtkinson, Michael P.
dc.contributor.departmentOperations Research (OR)
dc.subject.authorBrownian bridge movement modelsen_US
dc.subject.authorparticle filteren_US
dc.subject.authorstochastic modelingen_US
dc.subject.authornonlinear filteringen_US
dc.subject.authorsimulationsen_US
dc.description.recognitionOutstanding Thesis
dc.description.serviceLieutenant, United States Navyen_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


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