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Global evaluation of focussed Bayesian fusion

: Sander, J.; Heizmann, M.; Goussev, I.; Beyerer, J.

Postprint urn:nbn:de:0011-n-1332790 (2.6 MByte PDF)
MD5 Fingerprint: de7a943e516a8f8ec607015f61f2ae79
Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 16.6.2010

Braun, J.J. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; Society for Imaging Science and Technology -IS&T-:
Multisensor, multisource information fusion. Architectures, algorithms, and applications : 7-8 April 2010, Orlando, Florida, United States
Bellingham, WA: SPIE, 2010 (Proceedings of SPIE 7710)
ISBN: 978-0-8194-8174-0
Paper 77100A
Conference "Multisensor, Multisource Information Fusion - Architectures, Algorithms, and Applications" <2010, Orlando/Fla.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

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.