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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. On Canonical Polyadic Decomposition of NonLinear Gaussian Likelihood Functions
 Institute of Electrical and Electronics Engineers IEEE: 21st International Conference on Information Fusion, FUSION 2018 : 1013 July 2018, Cambridge, United Kingdom Piscataway, NJ: IEEE, 2018 ISBN: 9781538643303 ISBN: 9780996452762 ISBN: 9780996452779 S.11071113 
 International Conference on Information Fusion (FUSION) <21, 2018, Cambridge> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer FKIE () 
Abstract
Nonlinear filtering arises in many sensor applications such as for instance robotics, military reconnaissance, advanced driver assistance systems and other safety and security data processing algorithms. Since a closedform of the Bayesian estimation approach is intractable in general, approximative methods have to be applied. Kalman or particle based approaches have the drawback of either a Gaussian approximation or a curse of dimensionality which both leads to a reduction in the performance in challenging scenarios. An approach to overcome this situation is state estimation using decomposed tensors. In this paper, a novel method to compute a nonlinear likelihood function in Canonical Polyadic Decomposition form is presented, which avoids the full expansion of the discretized state space for each measurement. An exemplary application in a radar scenario is presented.