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Time evolution for dynamic probabilistic tensors in hierarchical tucker decomposition form

 
: Govaers, F.

:

Institute of Electrical and Electronics Engineers -IEEE-:
10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 : July 8-11, 2018 in Sheffield, United Kingdom (Great Britain)
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-4752-3
ISBN: 978-1-5386-4753-0
S.16-20
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 non-linear filtering problems and can therefore not be solved by closed-form 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, high-dimensional 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.

: http://publica.fraunhofer.de/dokumente/N-520149.html