Least committed basic belief density induced by a multivariate Gaussian: Formulation with applications
hal.structure.identifier | Advanced Learning Evolutionary Algorithms [ALEA] | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | CARON, Francois | |
hal.structure.identifier | Defence Science and Technology Organisation [DSTO] | |
dc.contributor.author | RISTIC, Branko | |
hal.structure.identifier | Sequential Learning [SEQUEL] | |
hal.structure.identifier | LAGIS-SI | |
dc.contributor.author | DUFLOS, Emmanuel | |
hal.structure.identifier | Sequential Learning [SEQUEL] | |
hal.structure.identifier | LAGIS-SI | |
dc.contributor.author | VANHEEGHE, Philippe | |
dc.date.issued | 2008-06 | |
dc.identifier.issn | 0888-613X | |
dc.description.abstractEn | We consider here the case where our knowledge is partial and based on a betting density function which is n-dimensional Gaussian. The explicit formulation of the least committed basic belief density (bbd) of the multivariate Gaussian pdf is provided in the transferable belief model (TBM) framework. Beliefs are then assigned to hyperspheres and the bbd follows a khi-2 distribution. Two applications are also presented. The first one deals with model based classification in the joint speed-accel- eration feature space. The second is devoted to joint target tracking and classification: the tracking part is performed using a Rao-Blackwellized particle filter, while the classification is carried out within the developed TBM scheme. | |
dc.language.iso | en | |
dc.publisher | Elsevier | |
dc.subject.en | Belief function theory | |
dc.subject.en | Transferable belief model | |
dc.subject.en | Evidential theory | |
dc.subject.en | Multivariate Gaussian pdf | |
dc.subject.en | Target classification | |
dc.subject.en | Target tracking | |
dc.subject.en | Particle filtering | |
dc.title.en | Least committed basic belief density induced by a multivariate Gaussian: Formulation with applications | |
dc.type | Article de revue | |
dc.identifier.doi | 10.1016/j.ijar.2006.10.003 | |
dc.subject.hal | Informatique [cs]/Automatique | |
bordeaux.journal | International Journal of Approximate Reasoning | |
bordeaux.page | 419-436 | |
bordeaux.volume | 48 | |
bordeaux.issue | 2 | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-00782301 | |
hal.version | 1 | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-00782301v1 | |
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