Recent trends in physics-based and human-derived information fusion (PHIF) have amplified the capabilities of analysts; however with the big data opportunities there is a need for open architecture designs, methods of distributed team collaboration, and visualizations. In this paper, we explore recent trends in the information fusion to support user interaction and machine analytics. Challenging scenarios requiring PHIF include combing physics-based video data with human-derived text data for enhanced simultaneous tracking and identification. A driving effort would be to provide analysts with applications, tools, and interfaces that afford effective and affordable solutions for timely decision making. Fusion at scale should be developed to allow analysts to access data, call analytics routines, enter solutions, update models, and store results for distributed decision making.

Date Published: 4 May 2017
PDF: 12 pages
Proc. SPIE 10207, Next-Generation Analyst V, 1020706 (4 May 2017); doi: 10.1117/12.2264687

Published in SPIE Proceedings Vol. 10207:
Next-Generation Analyst V
Timothy P. Hanratty; James Llinas, Editor(s)