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Proceedings Paper

The QuEST for multi-sensor big data ISR situation understanding
Author(s): Steven Rogers; Jared Culbertson; Mark Oxley; H. Scott Clouse; Bernard Abayowa; James Patrick; Erik Blasch; John Trumpfheller
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Paper Abstract

The challenges for providing war fighters with the best possible actionable information from diverse sensing modalities using advances in big-data and machine learning are addressed in this paper. We start by presenting intelligence, surveillance, and reconnaissance (ISR) related big-data challenges associated with the Third Offset Strategy. Current approaches to big-data are shown to be limited with respect to reasoning/understanding. We present a discussion of what meaning making and understanding require. We posit that for human-machine collaborative solutions to address the requirements for the strategy a new approach, Qualia Exploitation of Sensor Technology (QuEST), will be required. The requirements for developing a QuEST theory of knowledge are discussed and finally, an engineering approach for achieving situation understanding is presented.

Paper Details

Date Published: 12 May 2016
PDF: 16 pages
Proc. SPIE 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, 98310G (12 May 2016); doi: 10.1117/12.2229722
Show Author Affiliations
Steven Rogers, Air Force Research Lab. (United States)
Jared Culbertson, Air Force Research Lab. (United States)
Mark Oxley, Air Force Institute of Technology (United States)
H. Scott Clouse, Air Force Research Lab. (United States)
Bernard Abayowa, Air Force Research Lab. (United States)
James Patrick, Air Force Research Lab. (United States)
Erik Blasch, Air Force Research Lab. (United States)
John Trumpfheller, United States Air Force (United States)


Published in SPIE Proceedings Vol. 9831:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII
Michael A. Kolodny; Tien Pham, Editor(s)

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