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Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations

Reinhardt, Marc; Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.

In data fusion theory, multiple estimates are combined to yield an optimal result. In this paper, the set of all possible results is investigated, when two random variables with unknown correlations are fused. As a first step, recursive processing of the set of estimates is examined. Besides set-theoretic considerations, the lack of knowledge about the unknown correlation coefficient is modeled as a stochastic quantity. Especially, a uniform model is analyzed, which provides a new optimization criterion for the covariance intersection algorithm in scalar state spaces. This approach is also generalized to multi-dimensional state spaces in an approximative, but fast and scalable way, so that consistent estimates are obtained.

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Volltext §
DOI: 10.5445/IR/1000035131
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator ISBN: 978-1-4577-0267-9
KITopen-ID: 1000035131
Erschienen in Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 5-8 July 2011
Verlag IEEE, Piscataway
Seiten 8 S.
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