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

TL;DR

This paper evaluates supervised machine learning models using simulation data to determine optimal missile launch timing in beyond visual range air combat, highlighting the impact of resampling techniques on model performance.

Contribution

It demonstrates the effectiveness of decision tree-based models and the benefits of resampling techniques in improving missile launch prediction accuracy.

Findings

Decision tree models achieved high F1-scores.

Resampling techniques increased F1-score by 22.69%.

Models can support decision-making in air combat scenarios.

Abstract

This work compares supervised machine learning methods using reliable data from constructive simulations to estimate the most effective moment for launching missiles during air combat. We employed resampling techniques to improve the predictive model, analyzing accuracy, precision, recall, and f1-score. Indeed, we could identify the remarkable performance of the models based on decision trees and the significant sensitivity of other algorithms to resampling techniques. 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%. 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.