A Data-Driven Approach to Estimate LEO Orbit Capacity Models

• URL: http://arxiv.org/abs/2507.19365v1
• Date: Fri, 25 Jul 2025 15:16:54 GMT
• Title: A Data-Driven Approach to Estimate LEO Orbit Capacity Models
• Abstract summary: The population of resident space objects, divided into Active, Derelict, and Debris, in can be accurately modeled to predict future satellite and debris propagation.<n>This proposed approach makes use of a data set coming from a computational expensive high-fidelity model, the MOCAT-MC, to provide a light, low-fidelity counterpart that provides accurate forecasting in a shorter time frame.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Utilizing the Sparse Identification of Nonlinear Dynamics algorithm (SINDy) and Long Short-Term Memory Recurrent Neural Networks (LSTM), the population of resident space objects, divided into Active, Derelict, and Debris, in LEO can be accurately modeled to predict future satellite and debris propagation. This proposed approach makes use of a data set coming from a computational expensive high-fidelity model, the MOCAT-MC, to provide a light, low-fidelity counterpart that provides accurate forecasting in a shorter time frame.

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