AARDIS researched advanced electronic warfare (EW) training methods and tools that reduce the cognitive workload of instructors, improve training effectiveness, accelerate improvements in student performance, and reduce training costs.
Pilot training at the U.S. Air Force (USAF) faces two fundamental challenges: rising training costs and restrictions, and the changing nature of military conflicts around the globe. According to a recent RAND study, the costs of USAF initial skills training (IST) have risen to approximately $750 million per year. In addition, the opportunities for live training exercises, because of limits on training airspace, rising fuel costs and heightened security, have been diminishing.
These challenges, along with the change in military engagements from large-scale theater warfare to small regional conflicts characterized by smaller deployed forces, asymmetric threats, and protracted peace keeping operations, means that war fighters–and their training regimes–must become more adaptable. Crews support long deployments that often provide limited opportunities for training, and day-to-day operations predominantly deal with asymmetric threats that little resemble the more sophisticated engagements in other parts of the world. These conditions produce a gradual attrition of skills that could lead to more significant problems, including reduced force readiness and more training time for already stretched resources.
The goal of the After-action Review and Debrief Intelligent Support (AARDIS) project was to successfully develop and deploy advanced electronic warfare (EW) training methods and tools that reduce the cognitive workload of instructors, improve training effectiveness, accelerate improvements in student performance, and reduce costs. The AARDIS technology augments current high-fidelity Air Force EW training technology and is easily configurable and adaptable to various training platform requirements.
The AARDIS initiative investigated the feasibility of an intelligent “trace walker” capability that helps mark noteworthy events in the training exercise data stream (e.g., deviations from standard procedures), captures key metrics, analyzes student performance, and provides constructive feedback for use in real-time and in after-action reviews/debriefs. The trace walker captures observations from different points of view (e.g., blue, red, incoming missile) and intelligently captures the types of cues an expert instructor would note (e.g., classical mistakes to avoid).
The initiative also investigated how to best evaluate student training needs, provide targeted feedback, and generate new scenario parameters that provide focused training where and when it will make the most impact.