SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Guo, Zhixin Shi, Qi Xu, Xiaofan Shan, Sixiang Qin, Limin Ge, Linqiang Zhang, Rui Dai, Ya Zhu, Hua Jiang, Guowei |
| author_facet | Guo, Zhixin Shi, Qi Xu, Xiaofan Shan, Sixiang Qin, Limin Ge, Linqiang Zhang, Rui Dai, Ya Zhu, Hua Jiang, Guowei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics 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. |
| title | SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2506.13034 |