Abstract
Abstract : 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 organization. In this study, cognitive models were developed of 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.
Published Version
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