Adaptive Data Fusion for Real-time Threat Assessment
We propose a 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 map the results of advanced feature extraction algorithms (weighted multi-dimensional feature vectors) onto a nonlinear vector space which will increase separation and improve Pcc. We will investigate several different metrics of the utility of data fusion in addressing strategic and tactical courses of action. In addition, we will develop new techniques for feature adaptation and selection based upon current operational scenarios within the battle space.
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