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Efficient Multi-Threaded Data Starting Point Matching Method for Space Target Cataloging

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Title: Efficient Multi-Threaded Data Starting Point Matching Method for Space Target Cataloging
Authors: Jiannan Sun, Zhe Kang, Zhenwei Li, Cunbo Fan
Source: Sensors ; Volume 25 ; Issue 8 ; Pages: 2367
Publisher Information: Multidisciplinary Digital Publishing Institute
Publication Year: 2025
Collection: MDPI Open Access Publishing
Subject Terms: optical data processing, orbit prediction, data matching, multi-threaded technology, space targets
Description: Currently, multi-target survey telescope arrays play an important role in the build-up and maintenance of space object catalog databases, collecting massive observational data without attributing information. However, the matching process of massive observational data poses significant challenges to traditional prediction methods. To address the issues of low matching success rates and prolonged computation times in traditional methods, this paper proposes a multi-threaded data starting point matching method. First, orbital elements from the Space Surveillance and Tracking (SST) database are extracted for two days before and after the observation moment. A set of orbital elements closest to the observation epoch is filtered to form the primary candidate catalog containing the maximum number of objects. A matching error threshold is set. Second, multi-threaded traversal of the primary candidate catalog is performed to calculate observation residuals with the data starting point using an orbit prediction procedure. Orbital elements meeting the triple matching error threshold are selected to form the secondary candidate catalog, which is used in the entire data arc segment-matching calculation. Finally, the root mean square error (RMSE) of observation residuals for the entire data arc segment is computed point by point. The orbital elements satisfying the matching threshold are identified as matching results based on the principle of optimality. Experimental results demonstrate that with a matching error threshold of 1°, the proposed method achieves an average matching success rate of 97.62% for data arc segments with nearly 10,000 passes per day over 8 consecutive days. In the SST database containing an average of 25,720 targets, this method processes an average of 2164 data arc segments per minute, improving matching efficiency by 115 times compared to traditional prediction methods.
Document Type: text
File Description: application/pdf
Language: English
Relation: Physical Sensors; https://dx.doi.org/10.3390/s25082367
DOI: 10.3390/s25082367
Availability: https://doi.org/10.3390/s25082367
Rights: https://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.93953ED2
Database: BASE
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  – Url: https://doi.org/10.3390/s25082367#
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Efficient Multi-Threaded Data Starting Point Matching Method for Space Target Cataloging
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jiannan+Sun%22">Jiannan Sun</searchLink><br /><searchLink fieldCode="AR" term="%22Zhe+Kang%22">Zhe Kang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhenwei+Li%22">Zhenwei Li</searchLink><br /><searchLink fieldCode="AR" term="%22Cunbo+Fan%22">Cunbo Fan</searchLink>
– Name: TitleSource
  Label: Source
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  Data: Sensors ; Volume 25 ; Issue 8 ; Pages: 2367
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Multidisciplinary Digital Publishing Institute
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
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  Label: Collection
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  Data: MDPI Open Access Publishing
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22optical+data+processing%22">optical data processing</searchLink><br /><searchLink fieldCode="DE" term="%22orbit+prediction%22">orbit prediction</searchLink><br /><searchLink fieldCode="DE" term="%22data+matching%22">data matching</searchLink><br /><searchLink fieldCode="DE" term="%22multi-threaded+technology%22">multi-threaded technology</searchLink><br /><searchLink fieldCode="DE" term="%22space+targets%22">space targets</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Currently, multi-target survey telescope arrays play an important role in the build-up and maintenance of space object catalog databases, collecting massive observational data without attributing information. However, the matching process of massive observational data poses significant challenges to traditional prediction methods. To address the issues of low matching success rates and prolonged computation times in traditional methods, this paper proposes a multi-threaded data starting point matching method. First, orbital elements from the Space Surveillance and Tracking (SST) database are extracted for two days before and after the observation moment. A set of orbital elements closest to the observation epoch is filtered to form the primary candidate catalog containing the maximum number of objects. A matching error threshold is set. Second, multi-threaded traversal of the primary candidate catalog is performed to calculate observation residuals with the data starting point using an orbit prediction procedure. Orbital elements meeting the triple matching error threshold are selected to form the secondary candidate catalog, which is used in the entire data arc segment-matching calculation. Finally, the root mean square error (RMSE) of observation residuals for the entire data arc segment is computed point by point. The orbital elements satisfying the matching threshold are identified as matching results based on the principle of optimality. Experimental results demonstrate that with a matching error threshold of 1°, the proposed method achieves an average matching success rate of 97.62% for data arc segments with nearly 10,000 passes per day over 8 consecutive days. In the SST database containing an average of 25,720 targets, this method processes an average of 2164 data arc segments per minute, improving matching efficiency by 115 times compared to traditional prediction methods.
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  Data: Physical Sensors; https://dx.doi.org/10.3390/s25082367
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  Data: 10.3390/s25082367
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  Data: https://creativecommons.org/licenses/by/4.0/
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        Value: 10.3390/s25082367
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: optical data processing
        Type: general
      – SubjectFull: orbit prediction
        Type: general
      – SubjectFull: data matching
        Type: general
      – SubjectFull: multi-threaded technology
        Type: general
      – SubjectFull: space targets
        Type: general
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      – TitleFull: Efficient Multi-Threaded Data Starting Point Matching Method for Space Target Cataloging
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            NameFull: Jiannan Sun
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            NameFull: Zhe Kang
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            NameFull: Zhenwei Li
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            NameFull: Cunbo Fan
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              Y: 2025
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            – TitleFull: Sensors ; Volume 25 ; Issue 8 ; Pages: 2367
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