A local approach for focussed Bayesian fusion

Jennifer Sander; Michael Heizmann; Igor Goussev; Jürgen Beyerer

doi:10.1117/12.820165

13 April 2009 A local approach for focussed Bayesian fusion

Jennifer Sander, Michael Heizmann, Igor Goussev, Jürgen Beyerer

Proceedings Volume 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009; 73450N (2009) https://doi.org/10.1117/12.820165
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential.

Citation Download Citation

Jennifer Sander, Michael Heizmann, Igor Goussev, and Jürgen Beyerer "A local approach for focussed Bayesian fusion", Proc. SPIE 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009, 73450N (13 April 2009); https://doi.org/10.1117/12.820165

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