An entropy method for multisource data fusion

The present paper proposes a generic model of the multisource data fusion in the framework of the theory of information, with closer attention being given the different nature of data processed in common cases. This model that we have called entropy model is then used to elaborate processing methods able to face specific problems that may arise when multisource systems are implemented to achieve functions like classification and pattern recognition, matching of ambiguous observations, estimation, detection or tracking. Crucial practical problems to data fusion are more specifically dealt with, such as information representation, appropriate combination processing and decision, making. Some clues are given on the practical use and implementation of such an approach, for example, in the distributed estimation problem.