SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis

2025-06-16

AI Summary

To address the challenges of maneuver prediction and collision risk assessment for rapidly deploying low-Earth-orbit (LEO) satellite constellations (e.g., Starlink), existing studies are hindered by the lack of publicly available, realistic, and temporally complete orbital datasets. This work introduces the first open-source, time-series dataset specifically designed for satellite orbit analysis. It systematically integrates Two-Line Elements (TLEs) with high-precision ephemerides (e.g., JPL DE series), followed by propagation via SGP4/SDP4, temporal alignment, and multi-source calibration to accurately characterize representative orbital maneuvers. The dataset fills a critical gap in publicly accessible data for modeling real-world LEO satellite maneuvers and supports rigorous evaluation of diverse detection algorithms. In collision warning tasks, it improves early identification accuracy by 12.7% compared to baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Time-Series/Data Streams

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Lack of public datasets for space object maneuver prediction
• Need for high-precision orbital data and satellite dynamics analysis
• Addressing collision risk assessment in congested orbital environments

Innovation

• Integrates TLE and high-precision ephemeris data
• Models multidimensional satellite behavior realistically
• Supports maneuver detection and collision risk evaluation