Academic Journal
Deriving Thermospheric Density from GNSS POD: An Orthogonal Polynomial Differentiation Approach for Accelerometer-Free LEO Satellites—A High-Fidelity Simulation Study.
| Τίτλος: | Deriving Thermospheric Density from GNSS POD: An Orthogonal Polynomial Differentiation Approach for Accelerometer-Free LEO Satellites—A High-Fidelity Simulation Study. |
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| Συγγραφείς: | Yao, Liang, Zhang, Yasheng, Tao, Xuefeng, Zhang, Zhongtao, Zhao, Shuailong, Jiang, Yawen |
| Πηγή: | Aerospace (MDPI Publishing); Aug2026, Vol. 13 Issue 8, p682, 22p |
| Θεματικοί όροι: | Atmospheric density, Orthogonal polynomials, Legendre's polynomials, Low earth orbit satellites, Orbit determination |
| Περίληψη: | Accurate thermospheric mass density is crucial for precision orbit determination, trajectory prediction, and collision warning in the increasingly congested Low Earth Orbit (LEO) region. Traditional high-precision instantaneous inversion methods rely on dedicated onboard accelerometers (e.g., CHAMP and GRACE satellites), which are costly, scarce, and unable to meet the expansive monitoring needs of modern mega-constellations. In recent years, orbit data inversion methods based on high-precision orbit determination data have been extensively studied. Among them, the interpolation–differentiation method, although capable of retrieving instantaneous atmospheric density, severely amplifies position errors and measurement noise, significantly limiting retrieval accuracy. To address this gap, this study proposes a novel atmospheric density retrieval scheme for accelerometer-free LEO satellites, introducing an orthogonal polynomial (Legendre and Chebyshev) sliding-window fitting approach. The proposed method is evaluated using high-precision simulated orbit data. Under instantaneous inversion conditions, the Legendre method yields an average relative error (MRE) of 16.55%, a normalized root mean square deviation (NRMSD) of 12.80%, and a correlation coefficient R of 0.9370. To further evaluate its potential in climatological modeling, this paper also proposes a local solar time (LST) bin-averaging method to assess atmospheric inversion accuracy. Under this LST averaging approach, the inversion performance achieves an MRE of 6.83% and a correlation coefficient R exceeding 0.98. This demonstrates the feasibility of the inversion method for analyzing diurnal effects on atmospheric field evolution. [ABSTRACT FROM AUTHOR] |
| Copyright of Aerospace (MDPI Publishing) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 196614838 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42456054688 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deriving Thermospheric Density from GNSS POD: An Orthogonal Polynomial Differentiation Approach for Accelerometer-Free LEO Satellites—A High-Fidelity Simulation Study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yao%2C+Liang%22">Yao, Liang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yasheng%22">Zhang, Yasheng</searchLink><br /><searchLink fieldCode="AR" term="%22Tao%2C+Xuefeng%22">Tao, Xuefeng</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhongtao%22">Zhang, Zhongtao</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Shuailong%22">Zhao, Shuailong</searchLink><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Yawen%22">Jiang, Yawen</searchLink> – Name: TitleSource Label: Source Group: Src Data: Aerospace (MDPI Publishing); Aug2026, Vol. 13 Issue 8, p682, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Atmospheric+density%22">Atmospheric density</searchLink><br /><searchLink fieldCode="DE" term="%22Orthogonal+polynomials%22">Orthogonal polynomials</searchLink><br /><searchLink fieldCode="DE" term="%22Legendre's+polynomials%22">Legendre's polynomials</searchLink><br /><searchLink fieldCode="DE" term="%22Low+earth+orbit+satellites%22">Low earth orbit satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Orbit+determination%22">Orbit determination</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate thermospheric mass density is crucial for precision orbit determination, trajectory prediction, and collision warning in the increasingly congested Low Earth Orbit (LEO) region. Traditional high-precision instantaneous inversion methods rely on dedicated onboard accelerometers (e.g., CHAMP and GRACE satellites), which are costly, scarce, and unable to meet the expansive monitoring needs of modern mega-constellations. In recent years, orbit data inversion methods based on high-precision orbit determination data have been extensively studied. Among them, the interpolation–differentiation method, although capable of retrieving instantaneous atmospheric density, severely amplifies position errors and measurement noise, significantly limiting retrieval accuracy. To address this gap, this study proposes a novel atmospheric density retrieval scheme for accelerometer-free LEO satellites, introducing an orthogonal polynomial (Legendre and Chebyshev) sliding-window fitting approach. The proposed method is evaluated using high-precision simulated orbit data. Under instantaneous inversion conditions, the Legendre method yields an average relative error (MRE) of 16.55%, a normalized root mean square deviation (NRMSD) of 12.80%, and a correlation coefficient R of 0.9370. To further evaluate its potential in climatological modeling, this paper also proposes a local solar time (LST) bin-averaging method to assess atmospheric inversion accuracy. Under this LST averaging approach, the inversion performance achieves an MRE of 6.83% and a correlation coefficient R exceeding 0.98. This demonstrates the feasibility of the inversion method for analyzing diurnal effects on atmospheric field evolution. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Aerospace (MDPI Publishing) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/aerospace13080682 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 682 Subjects: – SubjectFull: Atmospheric density Type: general – SubjectFull: Orthogonal polynomials Type: general – SubjectFull: Legendre's polynomials Type: general – SubjectFull: Low earth orbit satellites Type: general – SubjectFull: Orbit determination Type: general Titles: – TitleFull: Deriving Thermospheric Density from GNSS POD: An Orthogonal Polynomial Differentiation Approach for Accelerometer-Free LEO Satellites—A High-Fidelity Simulation Study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yao, Liang – PersonEntity: Name: NameFull: Zhang, Yasheng – PersonEntity: Name: NameFull: Tao, Xuefeng – PersonEntity: Name: NameFull: Zhang, Zhongtao – PersonEntity: Name: NameFull: Zhao, Shuailong – PersonEntity: Name: NameFull: Jiang, Yawen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22264310 Numbering: – Type: volume Value: 13 – Type: issue Value: 8 Titles: – TitleFull: Aerospace (MDPI Publishing) Type: main |
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