esa/dSGP4
dSGP4: differentiable SGP4. Supports differentiability, ML integration & embarassingly parallel computations
This tool helps satellite operators, space traffic managers, and astrodynamicists predict the future position and velocity of satellites and other orbital objects using Two-Line Element (TLE) data. You input a TLE for an object and a specific time, and it outputs the object's predicted position and velocity. This is especially useful for tasks like calculating state transition matrices, transforming covariance, or estimating orbits with advanced machine learning techniques.
No commits in the last 6 months. Available on PyPI.
Use this if you need to propagate satellite orbits and require the ability to calculate derivatives (gradients) of these propagations for advanced analysis, optimization, or machine learning applications.
Not ideal if you simply need to propagate an orbit without any need for gradient calculations or integration with machine learning frameworks, as simpler SGP4 implementations might suffice.
Stars
82
Forks
17
Language
Python
License
GPL-3.0
Category
Last pushed
Apr 16, 2025
Commits (30d)
0
Dependencies
3
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