tetra3 is a fast lost-in-space plate solver for star trackers written in Python.
Use it to identify stars in images and get the corresponding direction (i.e. right ascension and declination) in the sky which the camera points to and indentification of the stars. tetra3 supports solving distorted images and a wide range of field of view. The default database is built for 10 to 30 degrees with stars down to magnitude 7, but you can easily build for any application down to a few degrees.
NOTE: In June 2023 themasterbranch of tetra3 was updated with a long list of improvements. This may cause some compatibility issues. Please use thelegacybranch in the interim and open an issue on GitHub to discuss changes and resolve any problems.
The software is available in the tetra3 GitHub repository. General instructions are available at the tetra3 ReadTheDocs website. tetra3 is Free and Open Source Software released by the European Space Agency under the Apache License 2.0. See LICENSE.txt in the repository for full licensing details.
Performance will vary, but in general solutions will take 10 milliseconds (excluding time to extract star positions from images) with 10 arcsecond (50 microradian) accuracy. The algorith is a pure lost-in-space solver, so it does not require any prior pointing information.
A camera with a field of view (FOV) of about 10 degrees and at least 512 by 512 pixels is a good starting point for general star tracking applications. Your camera should be able to acquire stars down to magnitude 7 for best results. (For a narrower FOV camera, you need to be able to capture dimmer stars.)
To effectively use tetra3 with your camera you may need to build a database optimised for your use case. The Yale Bright Star Catalog, and the Hipparcos and Tyco Catalogues are supported for generating databases; see the API documentation.
A real-world set of images acquired with a FLIR Blackfly S BFS-U3-31S4M-C (Sony IMX265 sensor; binned 2x2) camera and a Fujifilm HF35XA-5M 35mm f/1.9 lens are included as test data (11.4 degrees FOV).