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A local approach for focussed Bayesian fusion



Jennifer Sander
Michael Heizmann
Igor Goussev
Jürgen Beyerer


Belur Dasarathy (Hrsg.), Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, Proceedings of SPIE Vol. 7345, 2009.


SPIE Defense, Security + Sensing: Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, Orlando, USA, 16.-17. April 2009

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is detached from fixed modelling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate ourselves on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. We stress out that the term local does not necessarily imply spatial closeness. The resulting local models correspond to restricted versions of the original one in this contribution. In previous publications, 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. Based on the probabilistic error bounds for misleading evidence, 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.