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dc.contributor.authorTalebi, Sayed Pouria
dc.contributor.authorWerner, Stefan
dc.contributor.authorKoivunen, Visa
dc.date.accessioned2019-10-28T13:05:09Z
dc.date.available2019-10-28T13:05:09Z
dc.date.created2019-02-20T14:43:11Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-4658-8
dc.identifier.urihttp://hdl.handle.net/11250/2624921
dc.description.abstractIn this work, a distributed Kalman filtering and clustering framework for sensor networks tasked with tracking multiple state vector sequences is developed. This is achieved through recursively updating the likelihood of a state vector estimation from one agent offering valid information about the state vector of its neighbors, given the available observation data. These likelihoods then form the diffusion coefficients, used for information fusion over the sensor network. For rigour, the mean and mean square behavior of the developed Kalman filtering and clustering framework is analyzed, convergence criteria are established, and the performance of the developed framework is demonstrated in a simulation example.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartof2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.titleKalman Filtering and Clustering in Sensor Networksnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.identifier.doi10.1109/ICASSP.2018.8462039
dc.identifier.cristin1679249
dc.description.localcode© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,35,0
cristin.unitnameInstitutt for elektroniske systemer
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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