Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning
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 &...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10988533/ |
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| author | Joao P. A. Dantas Marcos R. O. A. Maximo Takashi Yoneyama |
| author_facet | Joao P. A. Dantas Marcos R. O. A. Maximo Takashi Yoneyama |
| author_sort | Joao P. A. Dantas |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-273a4df80b984cd58c1d69459547304d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-273a4df80b984cd58c1d69459547304d2025-08-20T03:07:06ZengIEEEIEEE Access2169-35362025-01-0113812048121710.1109/ACCESS.2025.356718610988533Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative LearningJoao P. A. Dantas0https://orcid.org/0000-0003-0300-8027Marcos R. O. A. Maximo1https://orcid.org/0000-0003-2944-4476Takashi Yoneyama2https://orcid.org/0000-0001-5375-1076Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, BrazilInstituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, BrazilInstituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, BrazilThis 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.https://ieeexplore.ieee.org/document/10988533/Air combat operationsgenerative learningimitation learningpop-up attack |
| spellingShingle | Joao P. A. Dantas Marcos R. O. A. Maximo Takashi Yoneyama Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning IEEE Access Air combat operations generative learning imitation learning pop-up attack |
| title | Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning |
| title_full | Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning |
| title_fullStr | Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning |
| title_full_unstemmed | Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning |
| title_short | Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning |
| title_sort | autonomous aircraft tactical pop up attack using imitation and generative learning |
| topic | Air combat operations generative learning imitation learning pop-up attack |
| url | https://ieeexplore.ieee.org/document/10988533/ |
| work_keys_str_mv | AT joaopadantas autonomousaircrafttacticalpopupattackusingimitationandgenerativelearning AT marcosroamaximo autonomousaircrafttacticalpopupattackusingimitationandgenerativelearning AT takashiyoneyama autonomousaircrafttacticalpopupattackusingimitationandgenerativelearning |