Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl
Abstract
:1. Introduction
2. Problem Statement
3. Methodology
Algorithm 1: GAN’s Gradient Descent Training. |
for a number of training iterations do |
for steps do |
• Pick a batch of number of noise samples {} from the prior noise . |
• Pick a batch of number of samples {} from the data generating distribution . |
• Update the discriminator’s weighting parameters by ascending gradient as: |
end for |
• Pick a batch of number of noise samples {} from the prior noise . |
• Update the discriminator’s weighting parameters by ascending gradient as: |
end for |
there is no restriction for utilizing the gradient descent algorithm. |
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
= | Discriminator network | |
= | Generator network | |
= | number of noise samples | |
= | Number of steps for future trajectory prediction | |
= | Number of executed transactions | |
= | Number associated with a block in the chain | |
= | Time during which the aircraft is as step n | |
= | Predicted Trajectory | |
= | Filter for feature extraction | |
= | Sample space | |
= | Probability space | |
= | Generator strategy | |
= | Generator’s weighing parameters |
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Specification | Value |
---|---|
Wing area Wingspan | 2.3 m2 4.2 m |
Mean aerodynamic chord Total length | 0.57 m 2.5 m |
Empty weight | 50 kg |
Maximum take-off weight | 80 kg |
Loitering airspeed | 35 knots |
Maximum speed | 135 knots |
Operational range Service ceiling | 120 km 15,000 ft |
Method | Training Accuracy (%) | Testing Accuracy (%) | Precision (%) | Sensitivity (%) | F1 Score (%) |
---|---|---|---|---|---|
LSTM | 94.9 | 90.6 | 97.2 | 93.6 | 93.7 |
GAN | 96.5 | 92.1 | 95.6 | 91.2 | 94.1 |
GAN-BLT | 95.7 | 91.6 | 94.1 | 94.3 | 96.4 |
Consensus Methodology Approaches | |||
---|---|---|---|
Deterministic | Leader-Based | Leader-Free | |
Error Rate % | 2.03 | 1.86 | 0.63 |
Iterations | 1 | 2 | 3 | 4 | 5 | |
---|---|---|---|---|---|---|
Fooling Rate [%] | LSTM methodology | 29.4 | 41.7 | 48.2 | 54.1 | 59.9 |
GAN methodology | 31.5 | 43.2 | 49.5 | 56.7 | 61.3 |
Iterations | 1 | 2 | 3 | 4 | 5 | |
---|---|---|---|---|---|---|
Fooling Rate [%] | AR-LSTM | 25.4 | 14.7 | 11.1 | 10.3 | 9.8 |
GAN | 27.3 | 12.5 | 8.4 | 5.5 | 4.8 | |
GAN-BLT | 13.7 | 7.6 | 4.8 | 2.9 | 2.8 |
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Hashemi, S.M.; Hashemi, S.A.; Botez, R.M.; Ghazi, G. Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl. Appl. Sci. 2023, 13, 9503. https://doi.org/10.3390/app13179503
Hashemi SM, Hashemi SA, Botez RM, Ghazi G. Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl. Applied Sciences. 2023; 13(17):9503. https://doi.org/10.3390/app13179503
Chicago/Turabian StyleHashemi, Seyed Mohammad, Seyed Ali Hashemi, Ruxandra Mihaela Botez, and Georges Ghazi. 2023. "Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl" Applied Sciences 13, no. 17: 9503. https://doi.org/10.3390/app13179503
APA StyleHashemi, S. M., Hashemi, S. A., Botez, R. M., & Ghazi, G. (2023). Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl. Applied Sciences, 13(17), 9503. https://doi.org/10.3390/app13179503