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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Time evolution for dynamic probabilistic tensors in hierarchical tucker decomposition form
 Institute of Electrical and Electronics Engineers IEEE: 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 : July 811, 2018 in Sheffield, United Kingdom (Great Britain) Piscataway, NJ: IEEE, 2018 ISBN: 9781538647523 ISBN: 9781538647530 S.1620 
 Sensor Array and Multichannel Signal Processing Workshop (SAM) <10, 2018, Sheffield> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer FKIE () 
Abstract
Many real world applications of target tracking and state estimation are nonlinear filtering problems and can therefore not be solved by closedform analytical solutions. In the recent past, tensor based approaches have become increasingly popular due to very effective decomposition algorithms, which allow the representation of discretized, highdimensional data in compressed form. In this paper, a solution of the prediction step for a Bayesian filter is proposed, where the probability density function (pdf) is approximated by a tensor in Hierarchical Tucker Decomposition. It is shown, that the computation of the predicted pdf is about five times faster than the previously proposed Canonical Polyadic Decomposition format.