Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Eh & eacute;catl

【Author】 Hashemi, Seyed Mohammad; Hashemi, Seyed Ali; Botez, Ruxandra Mihaela; Ghazi, Georges

【Source】APPLIED SCIENCES-BASEL

【影响因子】2.838

【Abstract】This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude, longitude, altitude, heading, speed, and time. The model's foundation is rooted in the Generative Adversarial Network (GAN) framework, known for its inherent generative capabilities, rendering it remarkably resilient against Adversarial Attacks. To enhance its credibility, the Blockchain is employed as a Ledger Technology (LT) to securely store legitimate predicted values utilized in subsequent trajectory predictions. The Blockchain ensures that only authorized and non-adversarial samples are stored in the blocks, rejecting any adversarial predictions. In the validation process, trajectory data for training the GAN model were generated through the UAS-S4 Ehecatl simulation model. The performance evaluation relies on the model's resistance to adversarial attacks, measured by fooling rates. The results acquired affirm the excellent efficacy of the GAN model, Secured by Blockchain, approaching against adversarial attacks.

【Keywords】robustness; aircraft trajectory prediction; generative adversarial networks; blockchain

【发表时间】2023 SEP

【收录时间】2023-09-22

【文献类型】实证数据

【主题类别】

区块链应用-实体经济-交通领域