The QuEST for multi-sensor big data ISR situation understanding

Steven Rogers; Jared Culbertson; Mark Oxley; H. Scott Clouse; Bernard Abayowa; James Patrick; Erik Blasch; John Trumpfheller

doi:10.1117/12.2229722

12 May 2016 The QuEST for multi-sensor big data ISR situation understanding

Steven Rogers, Jared Culbertson, Mark Oxley, H. Scott Clouse, Bernard Abayowa, James Patrick, Erik Blasch, John Trumpfheller

Author Affiliations +

Proceedings Volume 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII; 98310G (2016) https://doi.org/10.1117/12.2229722
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Steven Rogers, Jared Culbertson, Mark Oxley, H. Scott Clouse, Bernard Abayowa, James Patrick, Erik Blasch, and John Trumpfheller "The QuEST for multi-sensor big data ISR situation understanding", Proc. SPIE 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, 98310G (12 May 2016); https://doi.org/10.1117/12.2229722

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