SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis
Guo, Zhixin, Shi, Qi, Xu, Xiaofan, Shan, Sixiang, Qin, Limin, Ge, Linqiang, Zhang, Rui, Dai, Ya, Zhu, Hua, Jiang, Guowei
–arXiv.org Artificial Intelligence
With the rapid advancement of aerospace technology and the large-scale deployment of low Earth orbit (LEO) satellite constellations, the challenges facing astronomical observations and deep space exploration have become increasingly pronounced. As a result, the demand for high-precision orbital data on space objects-along with comprehensive analyses of satellite positioning, constellation configurations, and deep space satellite dynamics-has grown more urgent. However, there remains a notable lack of publicly accessible, real-world datasets to support research in areas such as space object maneuver behavior prediction and collision risk assessment. This study seeks to address this gap by collecting and curating a representative dataset of maneuvering behavior from Starlink satellites. The dataset integrates Two-Line Element (TLE) catalog data with corresponding high-precision ephemeris data, thereby enabling a more realistic and multidimensional modeling of space object behavior. It provides valuable insights into practical deployment of maneuver detection methods and the evaluation of collision risks in increasingly congested orbital environments.
arXiv.org Artificial Intelligence
Jun-17-2025
- Country:
- Asia > China
- Europe > Germany
- North America > United States
- California (0.04)
- South Carolina > Charleston County
- Charleston (0.04)
- Oceania > Australia
- South Australia > Adelaide (0.04)
- Genre:
- Research Report > New Finding (0.46)
- Industry:
- Aerospace & Defense (1.00)
- Government
- Military (0.46)
- Regional Government > North America Government
- United States Government (0.67)
- Space Agency (0.46)
- Technology: