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

arXiv: https://arxiv.org/abs/2207.04188 · HTML: https://mlsanity.com/paper/2207.04188/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 9 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Batch Normalized Recurrent Neural Networks](https://mlsanity.com/paper/1510.01378/) (2015)
- [Engagement Decision Support for Beyond Visual Range Air Combat](https://mlsanity.com/paper/2111.03059/) (2021)
- [ASA: A Simulation Environment for Evaluating Military Operational Scenarios](https://mlsanity.com/paper/2207.12084/) (2022)
- [Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network](https://mlsanity.com/paper/2111.04474/) (2021)

## Built on by (most cited first, 4 of 4)

- [Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2304.09669/) (2023)
- [Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards](https://mlsanity.com/paper/2302.05838/) (2023)
- [Maneuver Decision-Making Through Automatic Curriculum Reinforcement Learning Without Handcrafted Reward functions](https://mlsanity.com/paper/2307.06152/) (2023)
- [Real-Time Surface-to-Air Missile Engagement Zone Prediction Using Simulation and Machine Learning](https://mlsanity.com/paper/2311.11905/) (2023)

## Abstract (arXiv)

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.