Uncertainty modeling and its linguistic expression in data fusion systems

Presents a framework for handling uncertainty in data fusion systems, based on possibility theory. Information on units and observations is represented by a possibility distribution. The correlation problem is addressed, and the fusion process is described. Lastly, we present a linguistic interface, to translate possibility distributions into a natural language form, which can be understood by the operator.