Summary of Precise and Efficient Orbit Prediction in Leo with Machine Learning Using Exogenous Variables, by Francisco Caldas et al.
Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables
by Francisco Caldas, Cláudia Soares
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed machine learning algorithm improves Space Situational Awareness (SSA) by accurately predicting the orbits of spacecraft and space debris. By incorporating past positions and environmental variables like atmospheric density, the algorithm forecasts state vectors with low positioning errors at a very low computational cost. This is achieved through the utilization of machine learning and time-series techniques, outperforming conventional propagator methods like SGP4. The algorithm uses precision ephemeris data from the International Laser Ranging Service (ILRS) for almost a year, demonstrating its effectiveness in improving SSA capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Orbit prediction is crucial for Space Situational Awareness (SSA), which involves tracking space objects in Earth’s orbit. To improve accuracy, an algorithm uses past positions and environmental factors like atmospheric density to forecast the future path of spacecraft and debris. This helps prevent collisions and removes space junk. The method beats older techniques that don’t account for these factors well. |
Keywords
» Artificial intelligence » Machine learning » Precision » Time series » Tracking
