André Negrão Costa

Beyond Visual Range (BVR) air combat is an essential part of modern aerial warfare, relying on advanced radar, missile systems, and decision-support technologies. This survey provides a comprehensive overview of how simulation and Machine Learning (ML) tools have been used to analyze BVR combat, covering key methodologies, applications, and challenges. We examine how ML enables adaptive tactics to improve behavior recognition and threat assessment to enhance situational awareness. The paper also traces the historical evolution of BVR combat, outlining key engagement phases such as detection, missile launch, and post-engagement assessment. A key focus is on the role of simulation environments in modeling realistic combat scenarios, supporting pilot training, and validating AI-driven decision-making strategies. We analyze state-of-the-art simulation tools, comparing their capabilities and limitations for studying multi-agent coordination and real-time adaptability. This survey’s main contributions include descriptions of ML applications in BVR air combat, evaluations of simulation tools, identifications of research gaps, and insights into future research directions. This work provides an overview of how traditional simulation approaches merge with artificial intelligence to enable advanced, effective human and autonomous decision-making systems in dynamic and contested environments.

Satélites SAR fornecem diversos dados para apoio ao planejamento de missões de patrulha marítima, porém devido à resolução temporal de tais meios de vigilância e ao dinamismo intrínseco ao cenário marítimo, esses dados podem se tornar imprecisos em algumas horas. Este trabalho propõe uma solução computacional que atualiza probabilisticamente esse cenário projetando a posição dos alvos horas à frente, considerando diferentes tempos de reação dos meios de vigilância. Para tanto, o movimento de cada contato é modelado através de distribuições de probabilidade que representam seu comportamento provável e um cenário atualizado é projetado utilizando o método de Monte Carlo. Conclui-se que a utilização dessa metodologia permite analisar a persistência da aplicabilidade das informações coletadas e, inclusive, aponta para oportunidades operacionais do uso em situações de silêncio eletrônico.

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.