<p>Our modern society is more dependent on its assets in space now more than ever. For<br>
example, the Global Positioning System (GPS) many rely on for navigation uses data from a<br>
24-satellite constellation. Additionally, our current infrastructure for gas pumps, cell phones,<br>
ATMs, traffic lights, weather data, etc. all depend on satellite data from various constel-<br>
lations. As a result, it is increasingly necessary to accurately track and predict the space<br>
domain. In this thesis, after discussing how space object tracking and object position pre-<br>
diction is currently being done, I propose a machine learning-based approach to improving<br>
the space object position prediction over the standard SGP4 method, which is limited in<br>
prediction accuracy time to about 24 hours. Using this approach, we are able to show that<br>
meaningful improvements over the standard SGP4 model can be achieved using a machine<br>
learning model built based on a type of recurrent neural network called a long short term<br>
memory model (LSTM). I also provide distance predictions for 4 different space objects over<br>
time frames of 15 and 30 days. Future work in this area is likely to include extending and<br>
validating this approach on additional satellites to construct a more general model, testing a<br>
wider range of models to determine limits on accuracy across a broad range of time horizons,<br>
and proposing similar methods less dependent on antiquated data formats like the TLE.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/23557200 |
| Date | 23 June 2023 |
| Creators | Charles William Grey (16413678) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/LEVERAGING_MACHINE_LEARNING_FOR_ENHANCED_SATELLITE_TRACKING_TO_BOLSTER_SPACE_DOMAIN_AWARENESS/23557200 |
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