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[2602.16764] Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction
Summary
This article presents a machine learning model designed to correct latitude error in Low Earth Orbit (LEO) satellite propagation, enhancing orbit accuracy and uncertainty quantification.
Why It Matters
As reliance on LEO satellites for positioning and navigation increases, accurate orbit propagation becomes crucial. This research addresses the limitations of traditional Gaussian models in the presence of atmospheric drag, offering a machine learning solution that improves accuracy and extends the utility of existing satellite data.
Key Takeaways
- Machine learning can effectively correct latitude errors in LEO satellite orbits.
- The study compares a time-conditioned neural network with a Gaussian Process for error prediction.
- The proposed model maintains physics-based propagation while correcting dominant error growth.
Computer Science > Machine Learning arXiv:2602.16764 (cs) [Submitted on 18 Feb 2026] Title:Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction Authors:Alex Moody, Penina Axelrad, Rebecca Russell View a PDF of the paper titled Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction, by Alex Moody and 2 other authors View PDF HTML (experimental) Abstract:Low Earth orbit (LEO) satellites are leveraged to support new position, navigation, and timing (PNT) service alternatives to GNSS. These alternatives require accurate propagation of satellite position and velocity with a realistic quantification of uncertainty. It is commonly assumed that the propagated uncertainty distribution is Gaussian; however, the validity of this assumption can be quickly compromised by the mismodeling of atmospheric drag. We develop a machine learning approach that corrects error growth in the argument of latitude for a diverse set of LEO satellites. The improved orbit propagation accuracy extends the applicability of the Gaussian assumption and modeling of the errors with a corrected mean and covariance. We compare the performance of a time-conditioned neural network and a Gaussian Process on datasets computed with an open source orbit propagator and publicly available Vector Covariance Message (VCM) ephemerides. The learned models predict the argument of latitude error as a Gaussian distribution given parame...
