Abstract
Until recently, data fusion problems have been solved heuristically using fuzzy logic, rule-based inference, the Dempster-Shafer (DS) theory of evidence, etc. Beginning in the late 1970s, however, researchers have shown how data fusion can be placed under a purely probabilistic and theoretically rigorous paradigm based on the theory of (closed) random sets. The purpose of this talk is to provide a brief “mathematician’s overview” of this work, especially Finite Set Statistics (FISST). Random sets provide a natural setting for data fusion in two respects:
-
(a)
as a natural way of formulating Multi-Sensor (MS) Multi-Target (MT) problems
-
(b)
as a means of modelling ambiguous evidence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Reference
Goodman, LR., Mahler, R.P.S., and Nguyen, H.T., (1997) Mathematics of Data Fusion, Kluwer Academic
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Valin, P. (2002). Random Sets and Unification. In: Hyder, A.K., Shahbazian, E., Waltz, E. (eds) Multisensor Fusion. NATO Science Series, vol 70. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0556-2_10
Download citation
DOI: https://doi.org/10.1007/978-94-010-0556-2_10
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-0723-1
Online ISBN: 978-94-010-0556-2
eBook Packages: Springer Book Archive