Global evaluation of focussed Bayesian fusion

Jennifer Sander; Michael Heizmann; Igor Goussev; Jürgen Beyerer

doi:10.1117/12.850028

28 April 2010 Global evaluation of focussed Bayesian fusion

Jennifer Sander, Michael Heizmann, Igor Goussev, Jürgen Beyerer

Proceedings Volume 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010; 77100A (2010) https://doi.org/10.1117/12.850028
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Information fusion is essential for the retrieval of desired information in a sufficiently precise, complete, and robust manner. The Bayesian approach provides a powerful and mathematically funded framework for information fusion. By local Bayesian fusion approaches, the computational complexity of Bayesian fusion gets drastically reduced. This is done by a concentration of the actual fusion task on its probably most task relevant aspects. In this contribution, further research results on a special local Bayesian fusion technique called focussed Bayesian fusion are reported. At focussed Bayesian fusion, the actual Bayesian fusion task gets completely restricted to the probably most relevant parts of the range of values of the Properties of Interest. The practical usefulness of focussed Bayesian fusion is shown by the use of an example from the field of reconnaissance. Within this example, final decisions are based on local significance considerations and consistency arguments. As shown in previous publications, the absolute values of focussed probability statements represent upper bounds for their global values. Now, lower bounds which are obtained from the knowledge about the construction of the focussed Bayesian model are proven additionally. The usefulness of the resulting probability interval scheme is discussed.

Citation Download Citation

Jennifer Sander, Michael Heizmann, Igor Goussev, and Jürgen Beyerer "Global evaluation of focussed Bayesian fusion", Proc. SPIE 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010, 77100A (28 April 2010); https://doi.org/10.1117/12.850028

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