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Efficient Multi-Threaded Data Starting Point Matching Method for Space Target Cataloging
Jiannan Sun1,2, Zhe Kang1, Zhenwei Li1
1Changchun Observatory, National Astronomical Observatories, Chinese Academy of Sciences, Changchun 130117, China.
Sensors (Basel, Switzerland)
|April 26, 2025
Summary
This study introduces a multi-threaded method for matching space object data, significantly improving success rates and reducing computation time for cataloging. The new approach enhances efficiency for space surveillance and tracking (SST) databases.
Area of Science:
- Space Surveillance and Tracking (SST)
- Orbital Mechanics
- Computational Astronomy
Background:
- Multi-target survey telescope arrays generate vast amounts of unattributed observational data crucial for space object catalog maintenance.
- Traditional data matching methods struggle with massive datasets, leading to low success rates and long computation times.
- Accurate cataloging of space objects is vital for collision avoidance and space traffic management.
Purpose of the Study:
- To develop an efficient and accurate method for matching observational data to existing space object catalog entries.
- To overcome the limitations of traditional prediction methods in handling large-scale space surveillance data.
- To improve the speed and success rate of space object identification and catalog maintenance.
Main Methods:
- A multi-threaded data starting point matching method was developed, utilizing orbital elements from the Space Surveillance and Tracking (SST) database.
- Orbital elements closest to the observation epoch were filtered to create a primary candidate catalog.
- Multi-threaded traversal and orbit prediction were employed to calculate observation residuals, with a matching error threshold applied to form a secondary catalog.
- Root Mean Square Error (RMSE) of observation residuals was computed for final matching based on optimality.
Main Results:
- The proposed method achieved an average matching success rate of 97.62% for data arc segments over 8 days.
- With a matching error threshold of 1°, the system processed an average of 2164 data arc segments per minute.
- Matching efficiency was improved by 115 times compared to traditional prediction methods in an SST database with over 25,000 targets.
Conclusions:
- The multi-threaded data starting point matching method significantly enhances the efficiency and accuracy of space object cataloging.
- This approach effectively addresses the challenges posed by massive observational data in space surveillance.
- The developed method offers a substantial improvement for maintaining comprehensive and up-to-date space object databases.
