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Fusion based estimation of the a-priori probability distribution of unknown non-stationary processes

Junghans, Marek and Leich, Andreas (2019) Fusion based estimation of the a-priori probability distribution of unknown non-stationary processes. In: 2019 22nd International Conference on Information Fusion (FUSION), pp. 1-8. 22nd International Conference on Information Fusion, 2.-5. Juli 2019, Ottawa, Canada.

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Official URL: https://www.fusion2019.org/

Abstract

Non-stationary processes can be hard to handle, particular if one would like to know their characterizing time dependent probability functions. In this paper the a-priori probability distributions of unknown non-stationary processes are estimated with different combinations of weakly coupled sensors. For quantification of the unknown a-priori probabilities Bayesian Networks (BN) are adopted for data fusion and Dirichlet functions are applied on non-stationary, time-dependent maximum likelihood (ML) parameter learning. In several experiments the adaption of the non-stationary a-priori probability density functions is shown and the accuracy of data fusion regarding the underlying process variables with different characteristics are determined quantitatively. It is shown that the proposed algorithm can improve data fusion in case conditions for specific process and sensor characteristics are met.

Item URL in elib:https://elib.dlr.de/126481/
Document Type:Conference or Workshop Item (Speech)
Title:Fusion based estimation of the a-priori probability distribution of unknown non-stationary processes
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Junghans, MarekMarek.Junghans (at) dlr.dehttps://orcid.org/0000-0003-2019-401X
Leich, Andreasandreas.leich (at) dlr.dehttps://orcid.org/0000-0001-5242-2051
Date:4 July 2019
Journal or Publication Title:2019 22nd International Conference on Information Fusion (FUSION)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-8
Status:Published
Keywords:Bayesian Networks, Maximum-likelihood parameter learning, non-stationary processes
Event Title:22nd International Conference on Information Fusion
Event Location:Ottawa, Canada
Event Type:international Conference
Event Dates:2.-5. Juli 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - D.MoVe
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Data Management and Knowledge Discovery
Deposited By: Junghans, Marek
Deposited On:19 Aug 2019 12:26
Last Modified:09 Jul 2020 03:00

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