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On Canonical Polyadic Decomposition of Non-Linear Gaussian Likelihood Functions

 
: Govaers, F.

:

Institute of Electrical and Electronics Engineers -IEEE-:
21st International Conference on Information Fusion, FUSION 2018 : 10-13 July 2018, Cambridge, United Kingdom
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-4330-3
ISBN: 978-0-9964527-6-2
ISBN: 978-0-9964527-7-9
pp.1107-1113
International Conference on Information Fusion (FUSION) <21, 2018, Cambridge>
English
Conference Paper
Fraunhofer FKIE ()

Abstract
Non-linear 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 closed-form 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 non-linear 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.

: http://publica.fraunhofer.de/documents/N-520137.html