Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations

This work compares supervised machine learning methods using reliable data from beyond visual range air combat constructive simulations to estimate the most effective moment for launching missiles. We employed resampling techniques to improve the predictive model, and we could identify the remarkable performance of the models based on decision trees and the significant sensitivity of other algorithms. The models with the best f1-score brought values of 0.379 and 0.465 without and with the resampling technique, respectively, which is an increase of 22.69%, and an appropriate time inference. Thus, if desirable, resampling techniques can improve the model's recall and f1-score with a slight decline in accuracy and precision. Therefore, through data obtained through constructive simulations, it is possible to develop decision support tools based on machine learning models, which may improve the flight quality in BVR air combat, increasing the effectiveness of offensive missions to hit a particular target.