  This work aims to provide an engagement decision support tool for Beyond
Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA)
missions. In BVR air combat, engagement decision refers to the choice of the
moment the pilot engages a target by assuming an offensive stance and executing
corresponding maneuvers. To model this decision, we use the Brazilian Air
Force's Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao
Aeroespacial - ASA in Portuguese), which generated 3,729 constructive
simulations lasting 12 minutes each and a total of 10,316 engagements. We
analyzed all samples by an operational metric called the DCA index, which
represents, based on the experience of subject matter experts, the degree of
success in this type of mission. This metric considers the distances of the
aircraft of the same team and the opposite team, the point of Combat Air
Patrol, and the number of missiles used. By defining the engagement status
right before it starts and the average of the DCA index throughout the
engagement, we create a supervised learning model to determine the quality of a
new engagement. An algorithm based on decision trees, working with the XGBoost
library, provides a regression model to predict the DCA index with a
coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05
that can furnish parameters to the BVR pilot to decide whether or not to
engage. Thus, using data obtained through simulations, this work contributes by
building a decision support system based on machine learning for BVR air
combat.
