# Machine Learning in Orbit Estimation: a Survey

arXiv: https://arxiv.org/abs/2207.08993 · HTML: https://mlsanity.com/paper/2207.08993/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 155 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Bayesian Neural Networks: An Introduction and Survey](https://mlsanity.com/paper/2006.12024/) (2020)
- [Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement](https://mlsanity.com/paper/1908.11542/) (2019)
- [Learning Theory for Distribution Regression](https://mlsanity.com/paper/1411.2066/) (2014)
- [Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems](https://mlsanity.com/paper/1709.05409/) (2017)
- [Deep Super Learner: A Deep Ensemble for Classification Problems](https://mlsanity.com/paper/1803.02323/) (2018)
- [Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles](https://mlsanity.com/paper/2112.09051/) (2021)
- [Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application](https://mlsanity.com/paper/2201.02067/) (2022)
- [Improving Orbit Prediction Accuracy through Supervised Machine Learning](https://mlsanity.com/paper/1801.04856/) (2018)
- [Data-driven framework for real-time thermospheric density estimation](https://mlsanity.com/paper/1808.06014/) (2018)

## Built on by (most cited first, 8 of 8)

- [Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables](https://mlsanity.com/paper/2407.11026/) (2024)
- [Generalizing Trilateration: Approximate Maximum Likelihood Estimator for Initial Orbit Determination in Low-Earth Orbit](https://mlsanity.com/paper/2407.15180/) (2024)
- [SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis](https://mlsanity.com/paper/2506.13034/) (2025)
- [Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach](https://mlsanity.com/paper/2510.11242/) (2025)
- [Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction](https://mlsanity.com/paper/2602.16764/) (2026)
- [Physics-informed Conditional Normalizing Flows for Angles-only Cislunar Orbit Determination](https://mlsanity.com/paper/2606.30936/) (2026)
- [ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems](https://mlsanity.com/paper/2608.08804/) (2026)
- [Velocity-coupled Representation Refinement for Satellite Orbit Prediction](https://mlsanity.com/paper/2608.23728/) (2026)

## Abstract (arXiv)

Since the late 1950s, when the first artificial satellite was launched, the number of Resident Space Objects has steadily increased. It is estimated that around one million objects larger than one cm are currently orbiting the Earth, with only thirty thousand larger than ten cm being tracked. To avert a chain reaction of collisions, known as Kessler Syndrome, it is essential to accurately track and predict debris and satellites' orbits. Current approximate physics-based methods have errors in the order of kilometers for seven-day predictions, which is insufficient when considering space debris, typically with less than one meter. This failure is usually due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, and unknown characteristics such as the mass or geometry of the space object. Operators can enhance Orbit Prediction accuracy by deriving unmeasured objects' characteristics and improving non-conservative forces' effects by leveraging data-driven techniques, such as Machine Learning. In this survey, we provide an overview of the work in applying Machine Learning for Orbit Determination, Orbit Prediction, and atmospheric density modeling.