Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning

Joao P. A. Dantas, Marcos R. O. A. Máximo, Takashi Yoneyama

Abstract: This study presents a methodology for developing models that replicate the complex pop-up attack maneuver in air combat operations, using flight data from a Brazilian Air Force pilot in a 6-degree-of-freedom flight simulator. By applying imitation learning techniques and comparing three algorithms – Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) – the research trains models to predict aircraft control inputs through sequences of state-action pairs. The performances of these models were evaluated in terms of Root Mean Squared Error (RMSE), coefficient of determination (R2), training time, and inference time. To further enhance the training dataset with the aim of improving the robustness of the models, a Variational Autoencoder (VAE) was employed to generate synthetic data. These findings demonstrate the potential for deploying such models in fully autonomous aircraft, enhancing autonomous combat systems’ reliability and operational effectiveness in real-world scenarios.

External IDs:dblp:journals/access/DantasMY25