Towards context-aware hard/soft information fusion: Incorporating situationally qualified human observations into a fusion process for intelligence analysis

This paper describes a methodology for incorporating human observations into a hard+soft information fusion process for counterinsurgency intelligence analysis. The goal of incorporating human observations into the information fusion process is important as it extends the ability of the fusion algorithms to associate and merge disparate pieces of information by allowing for information collected from soft data sources (e.g., human observations) to be included in the process along with information collected from hard data sources (e.g., radar sensors). This goal is accomplished through the employment of fuzzy membership functions used in similarity scoring, for data association and situation assessment. These membership functions are based on situationally qualified error characteristics. Error characteristics represent the key to this process by allowing for accurate uncertainty alignment based on the known and/or unknown state of context dependent variables that have been empirically determined to influence the accuracy of human estimation for a given category- in this case human age estimation.