Machine Learning to Improve Situational Awareness in Beyond Visual Range Air Combat

Authors

  • Joao P. A. Dantas Decision Support Systems Subdivision, Institute for Advanced Studies, Sao Jose dos Campos - Sao Paulo, 12228-001, Brazil https://orcid.org/0000-0003-0300-8027
  • Marcos R. O. A. Maximo Autonomous Computational System Lab (LAB-SCA), Computer Science Division, Aeronautics Institute of Technology, Sao Jose dos Campos - Sao Paulo, 12228-900, Brazil https://orcid.org/0000-0003-2944-4476
  • Andre N. Costa Decision Support Systems Subdivision, Institute for Advanced Studies, Sao Jose dos Campos - Sao Paulo, 12228-001, Brazil https://orcid.org/0000-0001-9768-2340
  • Diego Geraldo Decision Support Systems Subdivision, Institute for Advanced Studies, Sao Jose dos Campos - Sao Paulo, 12228-001, Brazil https://orcid.org/0000-0003-1389-9142
  • Takashi Yoneyama Electronic Engineering Division, Aeronautics Institute of Technology, Sao Jose dos Campos - Sao Paulo, 12228-900, Brazil https://orcid.org/0000-0001-5375-1076

Keywords:

Artificial Intelligence, Machine Learning, Artificial Neural Networks, Data Science, Air Combat

Abstract

This article presents an artificial intelligence model using artificial neural networks that provide parameters to improve the situational awareness of a Beyond Visual Range (BVR) air combat pilot. In this combat modality, it is necessary to make decisions based on information from sensors, mainly radars. Furthermore, since information regarding enemy aircraft systems is sometimes unknown, pilots' decisions are usually based on beliefs regarding the opponent. The presented model proposes to deal with such characteristics, generating behaviors for entities represented in a constructive simulation environment, i.e., simulated people operating simulated systems. We created BVR air combat simulations between two aircraft, with only one missile each, through Latin Hypercube Sampling (LHS) to choose input variables to cover almost homogeneously all their ranges. The aircraft have similar behaviors, and their parameters may change only at the beginning of the simulation. The simulation environment generated ten thousand air combat scenarios, varying thirty-six input parameters, for the analysis proposed in the case study. From this data, we could create supervised machine learning models that substantially improve the BVR air combat pilot's situational awareness regarding offensive situations, in which the reference aircraft employs a missile against a target, or defensive positions, in contrast to when the same reference aircraft tries to avoid a possible enemy's missile launched in its direction. The offensive and defensive models were consistent with the accuracy of 0.930 and 0.924 and the F1-score of 0.717 and 0.678, respectively. Thus, the contribution of this work is to use machine learning algorithms to generate responses concerning the tactical state to improve the pilot's situational awareness and, therefore, the in-flight decision-making process.

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Author Biographies

Joao P. A. Dantas, Decision Support Systems Subdivision, Institute for Advanced Studies, Sao Jose dos Campos - Sao Paulo, 12228-001, Brazil

Joao P. A. Dantas received his B.Sc. degree in Mechanical-Aeronautical Engineering (2015) and his M.Sc. degree in Electronic and Computer Engineering (2018) from the Aeronautics Institute of Technology (ITA), Brazil. Currently, he is pursuing his Ph.D. degree at ITA and working as a researcher for the Brazilian Air Force at the Institute for Advanced Studies and the Robotics Institute at Carnegie Mellon University. His research interests include Simulation, Machine Learning, and Robotics.

Marcos R. O. A. Maximo, Autonomous Computational System Lab (LAB-SCA), Computer Science Division, Aeronautics Institute of Technology, Sao Jose dos Campos - Sao Paulo, 12228-900, Brazil

Marcos R. O. A. Maximo received the B.Sc. degree (Hons.) (summa cum laude) in Computer Engineering (2012), and the M.Sc. (2015) and Ph.D. (2017) degrees in Electronic and Computer engineering from the Aeronautics Institute of Technology (ITA), Brazil. He is currently a Professor at ITA, being a member of the Autonomous Computational Systems Laboratory (LAB-SCA) with research interests in Robotics and Artificial Intelligence.

Andre N. Costa, Decision Support Systems Subdivision, Institute for Advanced Studies, Sao Jose dos Campos - Sao Paulo, 12228-001, Brazil

Andre N. Costa received his B.Sc. degree in Mechanical-Aeronautical Engineering (2014) and his M.Sc. degree from the Graduate Program in Electronic and Computer Engineering (2019) from the Aeronautics Institute of Technology, Brazil. Currently, he is a researcher for the Brazilian Air Force at the Institute for Advanced Studies with research interests in Machine Learning, Modeling and Simulation.

Diego Geraldo, Decision Support Systems Subdivision, Institute for Advanced Studies, Sao Jose dos Campos - Sao Paulo, 12228-001, Brazil

Diego Geraldo was commissioned in 2005 as a graduate of the Brazilian Air Force (FAB) Academy and a fighter pilot in 2006. He received his M.Sc. degree from the Graduate Program in Aeronautical and Mechanical Engineering at the Aeronautics Institute of Technology in 2012. Since then he has been a researcher for FAB at the Institute for Advanced Studies, focusing on Artificial Intelligence, Modeling and Simulation.

Takashi Yoneyama, Electronic Engineering Division, Aeronautics Institute of Technology, Sao Jose dos Campos - Sao Paulo, 12228-900, Brazil

Takashi Yoneyama received the bachelor's degree in Electronic Engineering from the Aeronautics Institute of Technology (ITA), Brazil, in 1975, the M.D. degree in Medicine from the Taubate University, Brazil, in 1993, and the Ph.D. degree in Electrical Engineering from the Imperial College London, U.K., in 1983. He is a Professor of Control Theory with the Department of Electronic at ITA. His research is focused on Stochastic Optimal Control Theory.

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Published

2022-05-30

How to Cite

Dantas, J. P. A., Maximo, M. R. O. A., Costa, A. N., Geraldo, D., & Yoneyama, T. (2022). Machine Learning to Improve Situational Awareness in Beyond Visual Range Air Combat. IEEE Latin America Transactions, 100(XXX). Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6530