Latest revision as of 18:17, 3 February 2021

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.

Original document

The different versions of the original document can be found in:

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

• http://xplorestaging.ieee.org/ielx7/8442112/8454975/08455702.pdf?arnumber=8455702,

http://dx.doi.org/10.23919/icif.2018.8455702

• https://dblp.uni-trier.de/db/conf/fusion/fusion2018.html#Govaers18,

https://academic.microsoft.com/#/detail/2890518294