The Government’s “Cloud First” policy (Kundra, 2011) set an accelerated course of government technology migration to cloud resources. Traditional on-premises visual systems seem ripe for cloud migration because the systems are expensive, dedicated, compute-intensive machines with large physical footprints and requirements for regular maintenance, upgrades, and environment control.
Operation Blended Warrior (OBW) 2016 marked the second year of a three-year effort to document lessons learned and understand barriers to implementing Live, Virtual, Constructive (LVC) distributed training. In the first year of the event, LVC focus areas included connectivity, interoperability, data standards, after-action review, and cyber security. Year two introduced additional focus areas: multi-level security, cross domain solutions, long-haul feeds, and performance measurement. This paper focuses on this latter area—defining and collecting performance measures.
Several trends within the simulation and training industry are emphasizing the need for measurable proof that training solutions meet or exceed the requirements for delivering effective training. Cognitive state is a key component of learning, meaning that classification of cognitive state and capacity can provide a measure of training effectiveness. However, accurate classification of trainee state is an extremely challenging task. The more traditional subjective assessment methods have several limitations, while objective assessment methods can be difficult to implement.
The limited field of view of static egocentric visual displays employed in unmanned aircraft controls introduces the
soda straw effect on operators, which significantly affects their ability to capture and maintain situational awareness
by not depicting peripheral visual data.
The integration of virtual and constructive elements into live training not only opens new training avenues, but also
raises concerns about flight safety as live aircraft trainees need to be able to differentiate between live and virtual
entities and threats.
Training requirements for Real Beam Ground Map (RBGM) and Synthetic Aperture (SAR) Radar simulations
demand high-resolution terrain data in order to synthesize a Radar image. Radar simulators render the scene
differently than out-the-window rendering.
Given the reductions in Department of Defense budgets it is imperative that every dollar spent on training
warfighters be used in a cost efficient manner. One approach for cost effective training is distributed training
exercises that include live, virtual, and constructive participants, but injecting the training functionality into live
aircraft platforms is challenging.