Mary Fendley (Advisor), Subhashini Ganapathy (Committee Member), Kristen Liggett (Committee Member)
Master of Science in Engineering (MSEgr)
Difficulties with the implementation of persistent Wide Area Motion Imagery (WAMI) sensors to support real-time military missions have risen within Intelligence, Surveillance, and Reconnaissance organizations. In this study, cognitive models were developed of operators performing real-time missions currently supported by narrow field of view Full Motion Video (FMV) and WAMI sensors. These models were used in conjunction with a cognitive task analysis, creating an augmented operator function model (OFM-COG). This thesis describes the OFM-COG and demonstrates how this model-based analysis technique can document the cognitive implications of persistent surveillance with motion imagery. The analytic procedures required to build this model result in a methodology for the definition of an information display system specific for intelligence analysis tasks. Specifically, the models developed examine the cognitive demands of an Imagery Analyst (IA) during a real-time mission, with WAMI and/or FMV. From this, a set of cognitive metrics for analyst performance were identified for the real-time military missions in persistent surveillance.
Department or Program
Department of Biomedical, Industrial & Human Factors Engineering
Year Degree Awarded
Copyright 2013, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.