Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors
The chapter presents a game theoretic training model enabling a deep learning solution for rapid discovery of satellite behaviors from collected sensor data. The solution has two parts, namely, Part 1 and Part 2. Part 1 is a PE game model that enables data augmentation method, and Part 2 uses convol...
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| Main Authors: | , , , , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2020
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/72340 |
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| Summary: | The chapter presents a game theoretic training model enabling a deep learning solution for rapid discovery of satellite behaviors from collected sensor data. The solution has two parts, namely, Part 1 and Part 2. Part 1 is a PE game model that enables data augmentation method, and Part 2 uses convolutional neural networks (CNNs) for satellite behavior classification. The sensor data are propagated with the various maneuver strategies from the proposed space game models. Under the PE game theoretic framework, various satellite behaviors are simulated to generate synthetic datasets with labels for the training to detect space object behaviors. To evaluate the performance of the proposed PE model, a CNN model is designed and implemented for satellite behavior classification. Python 3 and TensorFlow are used in this implementation. The simulation results show that the trained machine learning model can efficiently and correctly classify the satellite behaviors up to 99.8%. |
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| ISBN: | 978-1-83968-373-2 |
| DOI: | 10.5772/intechopen.92636 |