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dc.contributor.authorLiang, Jiamingen
dc.contributor.authorGodsill, Sen
dc.date.accessioned2019-10-30T00:30:10Z
dc.date.available2019-10-30T00:30:10Z
dc.date.issued2019-05-01en
dc.identifier.isbn9781479981311en
dc.identifier.issn1520-6149
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/298178
dc.description.abstractIn this paper we present Bayesian methods for tracking scenarios in which an intrinsic coordinate model is considered and inertial mea- surements plus occasional position fixes are available. The methods are first tested using synthetic data, giving a comprehensive evalu- ation as to their performance. Further evaluation on real data also reveals our approaches can be favourable alternatives to existing in- ertial tracking/navigation models.
dc.rightsAll rights reserved
dc.rights.uri
dc.titleBayesian Fusion of Asynchronous Inertial, Speed and Position Data for Object Trackingen
dc.typeConference Object
prism.endingPage5216
prism.publicationDate2019en
prism.publicationNameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsen
prism.startingPage5212
prism.volume2019-Mayen
dc.identifier.doi10.17863/CAM.45232
dcterms.dateAccepted2019-02-01en
rioxxterms.versionofrecord10.1109/ICASSP.2019.8683469en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-05-01en
dc.contributor.orcidLiang, Jiaming [0000-0002-1318-4481]
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.freetoread.startdate2020-05-01


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