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Fast Fusion of Basic Belief Assignments Defined on a Dichotomous Frame of Discernment

Authors :
Deqiang Han
Albena Tchamova
Jean Dezert
Florentin Smarandache
DTIS, ONERA, Université Paris Saclay [Palaiseau]
ONERA-Université Paris-Saclay
UNIVERSITY OF NEW MEXICO GALLUP USA
Partenaires IRSTEA
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
Bulgarian Academy of Sciences
Institute of Integrated Automation, Jiatong Univ.
Source :
FUSION, 2020 IEEE 23rd International Conference on Information Fusion (FUSION), 2020 IEEE 23rd International Conference on Information Fusion (FUSION), Jul 2020, SUN CITY, South Africa. pp.1-8, ⟨10.23919/FUSION45008.2020.9190547⟩
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

International audience; In this paper, we propose a new fusion approach to combine basic belief assignments (BBAs) defined on a di-chotomous frame of discernment based on their canonical decomposition. In a companion paper, we have already proved that the canonical decomposition of this type of BBA (called dichotomous BBA) is always possible and unique thanks to the proportional conflict redistribution rule No 5 (PCR5). More precisely, any dichotomous BBA is always the PCR5 combination of two simpler basic belief assignments named respectively the pro-evidence, and the contra-evidence. From this interesting canonical decomposition, we present a new way of combining many dichotomous BBAs and we show that the computational time for fusing these dichotomous BBAs based on their canonical decomposition is quasi-linear with the number of sources to combine, contrary to the direct fusion of the dichotomous BBAs altogether.

Details

Database :
OpenAIRE
Journal :
2020 IEEE 23rd International Conference on Information Fusion (FUSION)
Accession number :
edsair.doi.dedup.....419ed9b67616eac58911e6791fa6178e
Full Text :
https://doi.org/10.23919/fusion45008.2020.9190547