One of the main goals of information fusion is uncertainty reduction. Quantification of uncertainty reduction depends on how uncertainty is represented. Uncertainty representation differs across the various Levels of Information Fusion (as defined by the JDL/DFIG models). For Level 1 Fusion, standard measures of uncertainty reduction are widely accepted in the community. For tracking, uncertainty reduction corresponds to reducing spatial (distance) and temporal (time) errors; for identification, the goal is to increase the probability of detection and reduce the probability of false alarms. However, for levels 2 and above (hereafter called High-Level Information Fusion – HFIL), the requirements and success criteria are not well standardized for the information fusion community.
High-level information fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgement that an HLIF framework must support automated knowledge representation and reasoning with uncertainty, there is no consensus on the requirements an HLIF framework must meet, on the most appropriate technologies to satisfy these requirements, and on how to evaluate how well they are being met. A clearly defined, scientifically rigorous evaluation framework and metrics are needed to help information fusion researchers assess the suitability of various approaches and tools to their applications. The ETUR working group is devoted to bring this evaluation framework to fruition.