The data fusion information group (DFIG) model is widely popular, extending and replacing the joint director of the labs (JDL) model as a data fusion processing framework that considers data/information exchange, user/team involvement, and mission/task design. The DFIG/JDL provides an initial design from which enhancements in analytics, learning, and teaming result in opportunities to improve data fusion methodologies. This paper highlights recent artificial intelligence/machine learning (AI/ML), deep learning, reinforcement learning, and active learning capabilities with that of the DFIG model for analysis and systems engineering designs. The general DFIG construct is applicable to many AI/ML systems; however, the focus of the paper provides useful considerations for the data fusion community to consider based on prior implemented approaches. The main ideas are: level 0 DFIG data preprocessing through AI/ML methods for data reduction, level 1/2/3 DFIG object/situation/impact assessment using AI/ML/DL methods for awareness, level 4 DFIG process refinement with reinforcement learning for control, and level 5/6 DFIG user/mission refinement with active learning for human-machine teaming.
|