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dc.contributor.advisorHealey, Anthony J.
dc.contributor.authorThorne, Richard L.
dc.date.accessioned2013-04-30T21:56:02Z
dc.date.available2013-04-30T21:56:02Z
dc.date.issued1997-03
dc.identifier.urihttp://hdl.handle.net/10945/31938
dc.description.abstractA truly Autonomous Vehicle must be able to determine its global position in the absence of external transmitting devices. This requires the optimal integration of all available organic vehicle attitude and velocity sensors. This thesis investigates the extended Kalman filtering method to merge asynchronous heading, heading rate, velocity, and DGPS information to produce a single state vector. Different complexities of Kalman filters, with biases and currents, are investigated with data from Florida Atlantic's Ocean Explorer II surface run. This thesis used a simulated loss of DGPS data to represent the vehicle's submergence. All levels of complexity of the Kalman filters are shown to be much more accurate then the basic dead reckoning solution commonly used aboard autonomous underwater vehicles.en_US
dc.description.urihttp://archive.org/details/asychronousiesyn1094531938
dc.format.extentxi, 154 p.;28 cm.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsApproved for public release, distribution unlimiteden_US
dc.subject.lcshUNDERWATER VEHICLESen_US
dc.subject.lcshKALMAN FILTERINGen_US
dc.subject.lcshDATA FUSIONen_US
dc.titleAsychronous [i.e. asynchronous] data fusion for AUV navigation using extended Kalman filtering.en_US
dc.title.alternativeAsynchronous data fusion for AUV navigation using extended Kalman filteringen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineering
dc.subject.authorNAen_US
dc.description.funderNAen_US
dc.description.recognitionNAen_US
etd.thesisdegree.nameM.S. in Mechanical Engineeringen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineMechanical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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