A library for simulating high-fidelity observations of satellite trajectories with configurable maneuvers and custom sensor networks.
MaDDG provides a simple interface for modeling complex observation scenarios. It allows you to create a satellite in any geocentric orbit, propagate its motion with a robust physical model, and track its position through optical sensors with customizable locations, observing limits, and noise parameters.
Through its use of hydra-zen and the submitit plugin, MaDDG can easily configure an array of simulation scenarios and distribute them in a SLURM cluster, empowering users to create large-scale, realistic datasets for training reliable maneuver detection and characterization models.
MaDDG is available on PyPI:
pip install MaDDGTo install from source, clone this repository and run the following command from its top-level directory:
pip install -e .If you want to modify the orbit propagation physics behind MaDDG, you will likely need to edit the AstroForge library, as well. AstroForge is an open-source astrodynamics library and a key requirement of MaDDG. See the AstroForge documentation for installation instructions.
For details on how to use the various features of MaDDG, we recommend following the Jupyter notebooks in the examples/ directory.
Please use this DOI number reference, published on Zenodo, when citing the software:
Use dropout.py to apply pseudo weather-based data dropout to dataset .csv files created with hz_launcher.py.
$ python scripts/dropout.py --help
usage: dropout.py [-h] --path PATH [--cloud_prob CLOUD_PROB] [--cloud_duration_mean CLOUD_DURATION_MEAN] [--cloud_duration_std CLOUD_DURATION_STD] [--num_runs NUM_RUNS]
[--save_copy_of_original] [--save_plots] [--submitit]
Script description
options:
-h, --help show this help message and exit
--path PATH The path to the input data file (.csv) (default: None)
--cloud_prob CLOUD_PROB
Probability of a cloud event blocking the sky during
any nighttime observable window for each sensor (default: 0.5)
--cloud_duration_mean CLOUD_DURATION_MEAN
Mean duration of a cloud event (seconds) (default: 10800.0)
--cloud_duration_std CLOUD_DURATION_STD
Standard deviation of a cloud event (seconds) (default: 3600.0)
--num_runs NUM_RUNS Number of dropout datasets to generate (default: 1)
--save_copy_of_original
Raise this flag to save copy of the original input data along
side of the modified dataset with dropouts (default: False)
--save_plots Raise this flag to generate and save plots (default: False)
--submitit Raise this flag to use submitit to launch jobs across multiple
nodes in parallel (default: False)
Example vscode launch.json entry:
{
"name": "Launch Dropout",
"type": "python",
"request": "launch",
"program": "scripts/dropout.py",
"console": "integratedTerminal",
"cwd": "/path/to/MaDDG",
"args": [
"--path=/path/to/complete.csv",
"--cloud_prob=1.0",
"--cloud_duration_mean=0,3600,7200,10800,14400,18000,21600,25200,28800,32400,36000,39600,43200,46800,50400,54000,57600",
"--cloud_duration_std=0.0",
"--num_runs=10",
"--save_copy_of_original",
"--submitit",
]
}DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
Research was sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, eitherexpressed or implied, of the United States Air Force or the U.S. Government. The U.S.Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
© 2024 Massachusetts Institute of Technology.