Deep Learning for Space Object Density Distribution
Prediction
Name
sarangerel-sschan01-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
9.51 MB
Format
Adobe PDF
Checksum (MD5)
1d513f74e9854834b2713f05dd1b3672
Author(s)
Sarangerel, Sumiyajav
Advisor(s)
Linares, Richard
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
The rapid growth of artificial objects in Low Earth Orbit (LEO) has heightened concerns over orbital congestion and collision cascades, known as Kessler Syndrome. Traditional high-fidelity models, while accurate, are computationally intensive and poorly scalable. This thesis introduces a machine learning–based framework for forecasting the long-term evolution of space object density. A large dataset is generated, using the MIT Orbital Capacity Assessment Tool – Monte Carlo (MOCAT-MC), simulating thousands of scenarios across varying launch, disposal, and maneuver parameters. A Convolutional Gated Recurrent Unit (ConvGRU) is trained to predict density distributions over a 100-year horizon, achieving accurate forecasts with significantly reduced runtime. With a simple guidance mechanism, the generalization capability of the model across diverse scenarios is greatly improved. This approach offers a scalable and efficient tool for supporting future space traffic management and sustainability efforts.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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