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dc.contributor.advisorChu, Peter C.
dc.contributor.advisorWettergren, Thomas A.
dc.contributor.advisorBetsch, Ronald E.
dc.contributor.authorColpo, Kristie M.
dc.dateSep-12
dc.date.accessioned2012-11-14T00:02:24Z
dc.date.available2012-11-14T00:02:24Z
dc.date.issued2012-09
dc.identifier.urihttp://hdl.handle.net/10945/17345
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractEvery mine countermeasures (MCM) operation is a balance of time versus risk. In attempting to reduce time and risk, it is in the interest of the MCM community to use unmanned, stationary sensors to detect and monitor drifting mines through harbor inlets and straits. A network of stationary sensors positioned along an area of interest could be critical in such a process by removing the MCM warfighter from a threat area and reducing the time required to detect a moving target. Although many studies have been conducted to optimize sensors and sensor networks for moving target detection, few of them considered the effects of the environment. In a drifting mine scenario, an oceanographic drift model could offer an estimation of surrounding environmental effects and therefore provide time critical estimations of target movement. These approximations can be used to further optimize sensor network components and locations through a defined methodology using estimated detection probabilities. The goal of this research is to provide such a methodology by modeling idealized stationary sensors and surface drift for the Hampton Roads Inlet.en_US
dc.description.urihttp://archive.org/details/jointsensingsamp1094517345
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.titleJoint Sensing/Sampling Optimization for Surface Drifting Mine Detection with High-Resolution Drift Modelen_US
dc.typeThesisen_US
dc.contributor.departmentMeteorology
dc.contributor.departmentPhysical Oceanography
dc.subject.authorDELFT3Den_US
dc.subject.authorDrift Modelen_US
dc.subject.authorMine Countermeasuresen_US
dc.subject.authorMonte Carlo Simulationen_US
dc.subject.authorOptimizationen_US
dc.subject.authorSensor Networksen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Meteorology And Physical Oceanographyen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineMeteorology and Physical Oceanographyen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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