Data fusion information group (DFIG) model meets AI+ML

Erik Blasch; Nichole Sullivan; Genshe Chen; Yu Chen; Dan Shen; Wei Yu; Hua-Mei Chen

doi:10.1117/12.2619624

8 June 2022 Data fusion information group (DFIG) model meets AI+ML

Erik Blasch, Nichole Sullivan, Genshe Chen, Yu Chen, Dan Shen, Wei Yu, Hua-Mei Chen

Proceedings Volume 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI; 121220N (2022) https://doi.org/10.1117/12.2619624
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Erik Blasch, Nichole Sullivan, Genshe Chen, Yu Chen, Dan Shen, Wei Yu, and Hua-Mei Chen "Data fusion information group (DFIG) model meets AI+ML", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 121220N (8 June 2022); https://doi.org/10.1117/12.2619624

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