Towards a Machine Learning-Based Approach to Predict Space Object Density Distributions
This work addresses congestion challenges for space operators in Low Earth Orbit, but it is incremental as it extends an existing tool with machine learning for computational efficiency.
The paper tackles the problem of predicting space object density distributions in Low Earth Orbit to address congestion from Anthropogenic Space Objects, proposing a machine learning-based model that accelerates propagation by training on hundreds of simulations from an established model, with experiments conducted over 100-year forecasting scenarios.
With the rapid increase in the number of Anthropogenic Space Objects (ASOs), Low Earth Orbit (LEO) is facing significant congestion, thereby posing challenges to space operators and risking the viability of the space environment for varied uses. Current models for examining this evolution, while detailed, are computationally demanding. To address these issues, we propose a novel machine learning-based model, as an extension of the MIT Orbital Capacity Tool (MOCAT). This advanced model is designed to accelerate the propagation of ASO density distributions, and it is trained on hundreds of simulations generated by an established and accurate model of the space environment evolution. We study how different deep learning-based solutions can potentially be good candidates for ASO propagation and manage the high-dimensionality of the data. To assess the model's capabilities, we conduct experiments in long term forecasting scenarios (around 100 years), analyze how and why the performance degrades over time, and discuss potential solutions to make this solution better.