The authors explore U.S. Air Force squadron restationing and range upgrade options that can maximize access to advanced live training ranges while evaluating cost and risk measures associated with these options.
Feb 5, 2021
Operationally Relevant Artificial Training for Machine Learning: Improving the Performance of Automated Target Recognition Systems
The authors explore whether an object-detection model trained on artificial images could evaluate real images as an automated target recognition system, an important advancement in artificial intelligence and machine learning for the U.S military.
Nov 18, 2020
This tool presents the technical details of an optimization model for the U.S. Air Force to analyze the potential effectiveness of different combinations of range upgrades and squadron restationing to improve access to advanced ranges.
Sep 24, 2020
Air Dominance Through Machine Learning: A Preliminary Exploration of Artificial Intelligence–Assisted Mission Planning
U.S. air superiority is being challenged by global competitors. In this report, the authors prototype a new artificial intelligence system to help develop and evaluate concepts of operations for the air domain.
May 29, 2020