The main goal of this research is to investigate the potential and current role of generative AI in improving and developing avionics systems, and aviation-related applications. Avionics, a vital part of aviation industry, consists of a set of equipment and electronic systems that operates the aircraft. Thus, development of these equipment is becoming increasingly difficult due to their complexity. In this manner, getting help from generative AI improves avionics systems and aviation applications in many areas from trajectory prediction, anomaly detection and data augmentation. This research aims to analyze current innovative solutions to the challenges faced in aviation applications exploring how generative AI models get integrated to them. This research’s focus is to investigate best practices of generative AI used up to now to increase the effect of aviation.

Keywords - Generative Adversarial Networks (GANs), Variational Auto Encoders (VAEs), Aviation, Anomaly Detection, Trajectory Prediction

[1] S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative ai,” Business amp; Information Systems Engineering, vol. 66, no. 1, p. 111–126, Sep.2023.[Online]. Available: http://dx.doi.org/10.1007/s12599-023-00834-7

[2] This Person Does Not Exist. (n.d.) This person does not exist. Accessed: [Date]. [Online]. Available: https://thispersondoesnotexist.com/

[3] R. Gozalo-Brizuela and E. C. Garrido-Merchan, “A survey of generative ´ ai applications,” 2023.

[4] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” 2014.

[5] D. P. Kingma and M. Welling, 2019.

[6] S. Liao, H. Ni, L. Szpruch, M. Wiese, M. Sabate-Vidales, and B. Xiao, “Conditional sig-wasserstein gans for time series generation,” 2023.

[7] Y. Chen, Q. Gao, and X. Wang, “Inferential wasserstein generative adversarial networks,” 2021.

[8] H. Zheng, X. Li, Y. Li, Z. Yan, and T. Li, “Gcn-gan: Integrating graph convolutional network and generative adversarial network for traffic flow prediction,” IEEE Access, vol. 10, pp. 94 051–94 062, 2022.

[9] S. Barua, S. M. Erfani, and J. Bailey, “Fcc-gan: A fully connected and convolutional net architecture for gans,” 2019.

[10] A. Carbonari, “Avionic systems overview,” in Proceedings. SBCCI 2004. 17th Symposium on Integrated Circuits and Systems Design (IEEE Cat. No.04TH8784), 2004, pp. 6–.

[11] Q. Hu, G. Huang, H. Shi, Y. Lin, and D. Guo, “A short-term aircraft trajectory prediction framework using conditional generative adversarial network,” in 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 2022, pp. 433–439.

[12] D.-T. Pham, T.-N. Tran, S. Alam, and V. N. Duong, “A generative adversarial imitation learning approach for realistic aircraft taxi-speed modeling,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2509–2522, 2022.

[13] Y. Liu and M. Hansen, “Predicting aircraft trajectories: A deep generative convolutional recurrent neural networks approach,” 2018.

[14] A. Bastas, T. Kravaris, and G. A. Vouros, “Data driven aircraft trajectory prediction with deep imitation learning,” 2020.

[15] T. Krauth, A. Lafage, J. Morio, X. Olive, and M. Waltert, “Deep generative modelling of aircraft trajectories in terminal maneuvering areas,” Machine Learning with Applications, vol. 11, p. 100446, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666827022001219

[16] S. M. Hashemi, S. A. Hashemi, R. M. Botez, and G. Ghazi, “Aircraft trajectory prediction enhanced through resilient generative adversarial networks secured by blockchain: Application to uas-s4 eheacute;catl,” Applied Sciences, vol. 13, no. 17, 2023. [Online]. Available: https://www.mdpi.com/2076-3417/13/17/9503

[17] X. Olive and L. Basora, “Identifying anomalies in past en-route trajectories with clustering and anomaly detection methods,” 06 2019.

[18] J. Du, L. Guo, L. Song, H. Liang, and T. Chen, “Anomaly detection of aerospace facilities using ganomaly,” in Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ser. ICMSSP ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 40–44. [Online]. Available: https://doi.org/10.1145/3404716.3404730

[19] M. Memarzadeh, B. Matthews, and I. Avrekh, “Unsupervised anomaly detection in flight data using convolutional variational auto-encoder,” Aerospace, vol. 7, no. 8, 2020. [Online]. Available: https://www.mdpi.com/2226-4310/7/8/115

[20] N. H. Campbell Jr., J. Grauer, and I. Gregory, “Loss of control detection for commercial transports using conditional variational autoencoders,” in SciTech 2021. Greenbelt, MD; Hampton, Virginia: NASA, 2021, extended Abstract.

[21] H. Ahn, D. Jung, and H.-L. Choi, “Deep generative modelsbased anomaly detection for spacecraft control systems,” Sensors, vol. 20, no. 7, 2020. [Online]. Available: https://www.mdpi.com/1424- 8220/20/7/1991

[22] X. Guo, C. Zhu, J. Yang, and Y. Xiao, “An anomaly detection model for ads-b systems using a lstm-based variational autoencoder,” in 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 2021, pp. 1005–1009

[23] L. Yang, “Conditional generative adversarial networks (cgan) for abnormal vibration of aero engine analysis,” in 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT, 2020, pp. 724–728.

[24] Q. Fu, H. Wang, J. Zhao, and X. Yan, “A maintenance-prediction method for aircraft engines using generative adversarial networks,” in 2019 IEEE 5th International Conference on Computer and Communications (ICCC), 2019, pp. 225–229.

[25] H. X. Cai, X. Y. Zhu, P. C. Wen, L. T. Bai, R. Q. Li, and W. Han, “Research on the application of generative adversarial networks in aerial image generation,” in 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 2022, pp. 416–420.

[26] Z. Wu and S. Meng, “An intelligent text processing method for civil aviation radiotelephony communication based on generative adversarial network,” in 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 2022, pp. 1–7.

[27] Y. Wang, L. Zhang, and J. Cui, “Gan-based wireless channel recognition enhancement in aerospace communication system,” Journal of Physics: Conference Series, vol. 1856, no. 1, p. 012036, apr 2021. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/1856/1/012036

[28] Q. Fu, H. Wang, and X. Yan, “Evaluation of the aeroengine performance reliability based on generative adversarial networks and weibull distribution,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 233, no. 15, pp. 5717–5728, 2019. [Online]. Available: https://doi.org/10.1177/0954410019856187>