Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning
Open Access
- 5 May 2025
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 13, 81204-81217
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- reedom 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.Keywords
Funding Information
- Article Processing Charge
- Coordination for the Improvement of Higher Education Personnel (CAPES)—Brazil (00x0ma614)
- National Research Council of Brazil (CNPq) through grants (304134/2-18-0, 307525/2022-8)
- respectively
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