An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods
Document Type
Article
Publication Date
11-16-2021
Abstract
The planning of individualized pilot training programs is an intensive process. Over 120 maneuvers are introduced into the training program over time while ensuring maneuver competencies. This work introduces a novel, deep-learning based approach for automatically generating training plans for pilot trainees to significantly reduce instructor pilot planning requirements. , The U.S. Air Force has a severe shortage of pilots. The Air Force’s Pilot Training Next (PTN) program seeks a more efficient pilot-training environment emphasizing the use of virtual reality flight simulators alongside periodic real aircraft experience. The objective of the PTN program is to accelerate the training pace and progress in undergraduate pilot training. Currently, instructor pilots spend excessive time planning and scheduling flights. This research focuses on methods to autogenerate the planning of in-flight events using hybrid filtering and deep learning techniques. The resulting approach captures temporal trends of user-specific and program-wide student performance to recommend a feasible set of graded flight events for evaluation in students’ next training exercise to improve their progress toward fully qualified status.
Source Publication
INFORMS Journal on Applied Analytics (ISSN 2644-0865, 2644-0873)
Recommended Citation
Nicholas C. Forrest, Raymond R. Hill, Phillip R. Jenkins (2021) An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods. INFORMS Journal on Applied Analytics 52(2):198-209.
Comments
Copyright © 2021, INFORMS
This article is accessible by subscription or purchase, using the DOI link below. It was published online in November 2021 ahead of inclusion in the March 2022 issue as cited below.