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dc.contributor.advisorXie, Geoffrey
dc.contributor.authorHorner, Douglas
dc.dateDec-13
dc.date.accessioned2014-02-18T23:38:59Z
dc.date.available2014-02-18T23:38:59Z
dc.date.issued2013-12
dc.identifier.urihttp://hdl.handle.net/10945/38947
dc.descriptionApproved for public release; distribution is unlimited.en_US
dc.description.abstractAccurate estimation and prediction of wireless signal strength holds the promise to improve a wide variety of applications in network-ing and unmanned systems. Current estimation approaches use either simplistic attenuation equations or detailed physical models that provide limited accuracy and may require a lengthy period of environmental assessment and computation. This dissertation presents a new, data-driven, stochastic framework for rapidly building accurate wireless connectivity maps. The framework advances the state of the art in three aspects. First, it augments the classic spatial interpolation procedure known as Kriging with a complementary additive approach to capture the typical anisotropic nature of wireless channels in cluttered environments. Second, it includes a technique for rapidly creating and maintaining a connectivity map in near real-time through the use of a spatial Bayesian recursive filter. Third, it introduces a novel methodology to adapt the resolution of a connectivity map based on the spatial characteristics and the quantity of available sample measurements. Detailed analyses, using several datasets collected recently in the Monterey Harbor, have confirmed the power and agility of the proposed approach.en_US
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. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleA data-driven framework for rapid modeling of wireless communication channelsen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Science
dc.subject.authorWireless Connectivity Mapsen_US
dc.subject.authorRandom Fieldsen_US
dc.subject.authorKrigingen_US
dc.subject.authorGaussian Process Modelsen_US
dc.subject.author`1 Regularized Logistic Regressionen_US
dc.subject.authorKalman Filteringen_US
dc.subject.authorUnderwater Acoustic Networkingen_US
etd.thesisdegree.nameDoctor Of Philosophy In Computer Scienceen_US
etd.thesisdegree.levelDoctoralen_US
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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