Dempster-Shafer theory and Bayesian reasoning in multisensor data fusion

Jerome J. Braun

doi:10.1117/12.381638

3 April 2000 Dempster-Shafer theory and Bayesian reasoning in multisensor data fusion

Jerome J. Braun

Proceedings Volume 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV; (2000) https://doi.org/10.1117/12.381638
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Bayesian and Dempster-Shafer Theory based methods are among the alternative algorithmic approaches to multisensor data fusion. The two approaches differ significantly and the extent of their applicability to data fusion is still being debated. This paper presents a Monte Carlo simulation approach for a comparative analysis of a Dempster-Shafer Theory based on a Bayesian multisensor data fusion in the classification task domain, including the implementation of both formalisms, and the results of the Monte Carlo experiments of this analysis.

Citation Download Citation

Jerome J. Braun "Dempster-Shafer theory and Bayesian reasoning in multisensor data fusion", Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); https://doi.org/10.1117/12.381638

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