Top ten trends in High-Level Information Fusion

High-Level Information Fusion (HLIF) is a relatively new exploration of methods in the last decade. The discussion will address the issues between low-level (signal processing and object state estimation and characterization) and HLIF (control, situational understanding, and relationships to the environment). From a series of efforts in identifying the main research focuses for the next decade, we have identified the main issues from fusion conference papers and panel discussions, towards a comprehensive analysis. With the advent of the HLIF grand challenges, many of the issues were analyzed over the last decade. In this paper, we highlight the main themes and a discussion of the attributes of the top ten issues. Since IF is to reduce uncertainty, a focus of this paper for the Evaluation of Techniques for Uncertainty Representation (ETUR) working group is to posit the issues of uncertainty for HLIF. Specific trends include data/knowledge representations, situation/threat/impact assessment, systems design, evaluation, and information management. The paper concludes with a topic of brief analysis of an uncertainty ontology for the ETURWG.