Use of Augmented Reality in Experimentation with New Equipment Training for Electro-Optic Infrared (EOIR) Sensors

Advanced Augmented Reality (AR) technologies that provide a real-to-life level of complexity are being explored for training target acquisition with electro-optical/infrared (EO/IR) images. An understanding of the perception errors imparted from the AR system is necessary to determine if the system can afford training. To meet this challenge, the development of TARgET, a Testbed for AR Experimentation and Training, is proposed. TARgET is an analysis tool that incorporates predictive and experimental methodologies that evaluate the applicability of AR technologies for use with EO/IR sensor target acquisition training and experimentation. The experiments comprised in TARgET include psychophysical metrics and an automated experimental design methodology for ensuring content and construct validity before then assessing training efficacy. The TARgET testbed is comprised of three modules: Predictive Assessment Module, Human Visual Perception Validation Module, and the Training Efficacy Evaluation Module. The software tool will store a database of specifications for different AR technologies and relevant human performance and psychophysical tests. A methodology for completing the user-centered experiments is provided, along with data analysis and interpretation. Results are stored and can be searched for best-in-class hardware and software combinations. Additionally, cross comparisons between training approaches using AR and non-AR strategies are possible.