Resident Space Object Light Curve Classification & Space Situational Awareness Sensitivity and Simulation Studies
| dc.contributor.advisor | Lee, Regina S. K. | |
| dc.contributor.author | Qashoa, Randa | |
| dc.date.accessioned | 2025-04-10T10:50:49Z | |
| dc.date.available | 2025-04-10T10:50:49Z | |
| dc.date.copyright | 2024-11-21 | |
| dc.date.issued | 2025-04-10 | |
| dc.date.updated | 2025-04-10T10:50:49Z | |
| dc.degree.discipline | Earth & Space Science | |
| dc.degree.level | Doctoral | |
| dc.degree.name | PhD - Doctor of Philosophy | |
| dc.description.abstract | The number of objects being launched into space is rapidly increasing, emphasizing the critical importance of detecting, characterizing, and tracking these objects—an area of focus known as Space Situational Awareness (SSA). These Resident Space Objects (RSOs) include satellites (both active and inactive), rocket bodies and debris. Knowing the type of object near our satellites of interest is very important as it gives satellite operators the knowledge needed to accurately plan maneuvers to keep our orbits safe. This dissertation explores three main contributions within the field of SSA. The first is a light curve classifier which uses Machine Learning (ML) to classify Low Earth Orbit (LEO) RSOs into stable satellites, tumbling satellites and rocket bodies. Multiple approaches were tested but the method with the highest accuracy is a Barlow Twins network which has a 75% accuracy for two minute light curves and a 97% accuracy for five minute light curves. The classification is used to characterize the motion of objects, which operators can use in combination with real images to determine the risk of collision and to perform effective maneuvers. The second contribution is regarding SSA mission planning. A sensitivity analysis was conducted to determine the best camera to use for observing co-orbiting RSOs within 250 km of the observer. The analysis includes exploring the location of potential targets in the Field-Of-View (FOV) of the observer as well as the Signal-to-Noise Ratio (SNR) of different targets. A similar analysis to the one presented in this dissertation has been performed for the Redwing microsatellite mission. Lastly, RSO image prediction simulations are tested for use in SSA. This dissertation demonstrated the implementation of an anti-sun pointing orientation for prediction simulations with validation from real images. Predicted images were used to determine targets for observation which were then validated following the downlink of the images. | |
| dc.identifier.uri | https://hdl.handle.net/10315/42819 | |
| dc.language | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject.keywords | Space Situational Awareness | |
| dc.subject.keywords | Light curve | |
| dc.subject.keywords | Machine learning | |
| dc.subject.keywords | Resident Space Object | |
| dc.title | Resident Space Object Light Curve Classification & Space Situational Awareness Sensitivity and Simulation Studies | |
| dc.type | Electronic Thesis or Dissertation |
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