Densely-Packed Target Data Fusion for Naval Mission-level Simulation Systems

We propose a principled data fusion framework that is appropriate for an adaptive classifier implemented with supervised and multi-task learning. The detection and data fusion (DDF) engine will incorporate a novel Bayes-optimal multiple target tracking system. We will investigate several different metrics of the utility of data fusion in addressing strategic and tactical course of actions. We will perform testing on measured data to help define which is the most appropriate for Navy multi-sensor missions. In addition, we will develop new techniques for feature adaptation and selection based upon current operational scenarios within the battle space.