2 citations · 3 across the 5 of their papers we have counts for
5 papers
Loyal Wingman Assessment: Social Navigation for Human-Autonomous Collaboration in Simulated Air Combat
Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama
This study proposes social navigation metrics for autonomous agents in air combat, aiming to facilitate their smooth integration into pilot formations. The absence of such metrics…
Real-Time Surface-to-Air Missile Engagement Zone Prediction Using Simulation and Machine Learning
Joao P. A. Dantas, Diego Geraldo, Felipe L. L. Medeiros +2
Surface-to-Air Missiles (SAMs) are crucial in modern air defense systems. A critical aspect of their effectiveness is the Engagement Zone (EZ), the spatial region within which a SA…
AsaPy: A Python Library for Aerospace Simulation Analysis
Joao P. A. Dantas, Samara R. Silva, Vitor C. F. Gomes +5
AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining…
ASA-SimaaS: Advancing Digital Transformation through Simulation Services in the Brazilian Air Force
Joao P. A. Dantas, Diego Geraldo, Andre N. Costa +2
This work explores the use of military simulations in predicting and evaluating the outcomes of potential scenarios. It highlights the evolution of military simulations and the inc…
Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach
Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama
This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper pres…