An Enhanced Mobile Device Based Navigation Model: Ubiquitous Computing

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dc.contributor.authorOlajubu, Emmanuel
dc.contributor.authorefiong, john E
dc.date.accessioned2023-05-13T16:22:35Z
dc.date.available2023-05-13T16:22:35Z
dc.date.issued2018-01
dc.descriptionTITLE:An Enhanced Mobile Device Based Navigation Model: Ubiquitous Computing VOLUME NO:9 ISSUE NO:2 JANUARY TO MARCH 2018 PAGES:23en_US
dc.description.abstractThisarticleformulated,simulatedandevaluatedanenhancedmodelforanon-linear/non-Gaussian integratedGlobalPositioningSystem(GPS)andInertialNavigationSystem(INS)mobile-baseddevice navigationsystemusingtheParticleFilter(PF).Thiswaswithaviewtoenhancingtheaccuracy andminimizethedelayexperiencedintheexistingsystemwhichreliesonlineardata.Anandroid drivenInfinixX5mobiledevicewithGPSandINS-basedsensorswasusedtoimplementthemodel formulated,standardBayesianestimatorswereusedtogeneratenon-lineardatasetswithGaussian/ non-GaussianwhitenoisesformobiledevicebasedINS/GPSsensors.Amathematicalmodelwas formulatedusingSamplingImportance-weightResampling(SIR)algorithmofthePF.Theconceptual modelwasdevelopedusingSimulinkandthedesignspecificationwasdoneusingUnifiedModeling Language(UML).ThemodelwassimulatedwithMATLABandthesimulationresultsobtainedwere evaluatedusingstandardmetrics,andbenchmarkedwithexistingmodel.Theoverallresultsshowed thattheproposedmodelperformedbetterthanexistingonesintermofaccuracy.However,themodel didnotimpactondelayreduction.en_US
dc.identifier.urihttps://ir.oauife.edu.ng/123456789/5215
dc.language.isoenen_US
dc.publisherIGI Globalen_US
dc.subjectGPS, INS, Kalman Filter, Navigation, Particle, Re-Sampling, SIRen_US
dc.titleAn Enhanced Mobile Device Based Navigation Model: Ubiquitous Computingen_US
dc.typeJournalen_US
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