Multi-Sensor Centralized Fusion Without Measurement Noise Covariance By Variational Bayesian Approximation

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dc.contributor.author Gao, Xiaoshan en_US
dc.contributor.author Chen, J en_US
dc.contributor.author Tao, Dacheng en_US
dc.contributor.author Li, X L en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T10:56:35Z
dc.date.available 2012-02-02T10:56:35Z
dc.date.issued 2011 en_US
dc.identifier 2010004794 en_US
dc.identifier.citation Gao Xiaoshan et al. 2011, 'Multi-Sensor Centralized Fusion Without Measurement Noise Covariance By Variational Bayesian Approximation', IEEE-Inst Electrical Electronics Engineers Inc, vol. 47, no. 1, pp. 718-727. en_US
dc.identifier.issn 0018-9251 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/15336
dc.description.abstract The work presented here solves the multi-sensor centralized fusion problem in the linear Gaussian model without the measurement noise variance. We generalize the variational Bayesian approximation based adaptive Kalman filter (VB_AKF) from the single sen en_US
dc.language en_US
dc.publisher IEEE-Inst Electrical Electronics Engineers Inc en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/TAES.2011.5705702 en_US
dc.title Multi-Sensor Centralized Fusion Without Measurement Noise Covariance By Variational Bayesian Approximation en_US
dc.parent IEEE Transactions On Aerospace And Electronic Systems en_US
dc.journal.volume 47 en_US
dc.journal.number 1 en_US
dc.publocation Piscataway, USA en_US
dc.identifier.startpage 718 en_US
dc.identifier.endpage 727 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 090600 en_US
dc.personcode 0000050074 en_US
dc.personcode 0000020247 en_US
dc.personcode 111502 en_US
dc.personcode 0000069806 en_US
dc.percentage 100 en_US
dc.classification.name Electrical and Electronic Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity WOS:000286931800048 en_US
dc.description.keywords Track Association; Particle Filters; Monte-Carlo; State; Parameters; Estimators; Tutorial en_US


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