The QuEST for multi-sensor big data ISR situation understanding
Steven K. Rogers, Jared L. Culbertson, Mark E. Oxley, Hamilton Scott Clouse, Bernard O. Abayowa, James A. Patrick, Erik Blasch, John Trumpfheller · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
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.