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Proceedings Paper

Unified approach to the fusion of imperfect data?
Author(s): Mihai Cristian Florea; Anne-Laure Jousselme; Dominic Grenier; Eloi Bosse
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Paper Abstract

For several years, researchers have explored the unification of the theories enabling the fusion of imperfect data and have finally considered two frameworks: the theory random sets and the conditional events algebra. Traditionally, the information is modeled and fused in one of the known theories: bayesian, fuzzy sets, possibilistic, evidential, or rough sets... Previous work has shown what kind of imperfect data these theories can best deal with. So, depending on the quality of the available information (uncertain, vague, imprecise, ...), one particular theory seems to be the preferred choice for fusion. However, in a typical application, the variety of sources provide different kinds of imperfect data. The classical approach is then to model and fuse the incoming data in a single theory being previously chosen. In this paper, we first introduce the various kinds of imperfect data and then the theories that can be used to cope with the imperfection. We also present the existing relationships between them and detail the most important properties for each theory. We finally propose the random sets theory as a possible framework for unification, and thus show how the individual theories can fit in this framework.

Paper Details

Date Published: 6 March 2002
PDF: 11 pages
Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); doi: 10.1117/12.458372
Show Author Affiliations
Mihai Cristian Florea, Univ. Laval (Canada)
Anne-Laure Jousselme, Defence Research Establishment Valcartier (Canada)
Dominic Grenier, Univ. Laval (Canada)
Eloi Bosse, Defence Research Establishment Valcartier (Canada)

Published in SPIE Proceedings Vol. 4731:
Sensor Fusion: Architectures, Algorithms, and Applications VI
Belur V. Dasarathy, Editor(s)

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