Incorporating situationally qualified human observations into a fusion process for intelligence analysis

Since my second year at UB my research assistantship has been funded by a 3 year multi-university research initiative (MURI) grant focused on network based hard/soft information fusion.  The first year of this research (for me at least) was focused on learning about how accurate people are at observing different phenomena in the world given specific contextual factors.  For example, how accurately can people judge distances?  What if it’s nighttime?  What if it’s snowing? etc.

This work was developed to allow human observations to be appropriately characterized in terms of their error/bias so that they could be integrated into a data fusion process along with hard data (which comes from things like radar sensors that are highly calibrated).  The poster below, which was created for a UB School of Engineering and Applied Sciences poster competition, describes the results of these research efforts at a high level.