TY - GEN
T1 - Data Fusion Information Group (DFIG) Model meets AI+ML
AU - Blasch, Erik
AU - Sullivan, Nichole
AU - Chen, Genshe
AU - Chen, Yu
AU - Shen, Dan
AU - Yu, Wei
AU - Chen, Hua Mei
N1 - Publisher Copyright: © 2022 SPIE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Active Learning. Automatic Target Recognition
KW - Data Fusion Information Group Model
KW - Deep Learning
KW - Information Fusion
KW - Reinforcement Learning
KW - User Refinement
UR - https://www.scopus.com/pages/publications/85136000056
U2 - 10.1117/12.2619624
DO - 10.1117/12.2619624
M3 - Conference contribution
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI
A2 - Kadar, Ivan
A2 - Blasch, Erik P.
A2 - Grewe, Lynne L.
PB - SPIE
T2 - Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI 2022
Y2 - 6 June 2022 through 12 June 2022
ER -