Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis
The accuracy and stability of Very Long Baseline Interferometry (VLBI) systems are essential for maintaining global geodetic reference frames such as the International Terrestrial Reference Frame (ITRF). This study focuses on the precise determination of the VLBI Invariant Point (IVP) and the detect...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| author | Chang-Ki Hong Tae-Suk Bae |
| author_facet | Chang-Ki Hong Tae-Suk Bae |
| author_sort | Chang-Ki Hong |
| collection | DOAJ |
| description | The accuracy and stability of Very Long Baseline Interferometry (VLBI) systems are essential for maintaining global geodetic reference frames such as the International Terrestrial Reference Frame (ITRF). This study focuses on the precise determination of the VLBI Invariant Point (IVP) and the detection of antenna axis offset. Ground-based surveys were conducted at the Sejong Space Geodetic Observatory using high-precision instruments, including total station, to measure slant distances, as well as horizontal and vertical angles from fixed pillars to reflectors attached to the VLBI instrument. The reflectors comprised both prisms and reflective sheets to enhance redundancy and data reliability. A detailed stochastic model incorporating variance component estimation was employed to manage the varying precision of the observations. The analysis revealed significant measurement variability, particularly in slant distance measurements involving prisms. Iterative refinement of the variance components improved the reliability of the IVP and antenna axis offset estimates. The study identified an antenna axis offset of 5.6 mm, which was statistically validated through hypothesis testing, confirming its significance at a 0.01 significance level. This is a significance level corresponding to approximately a 2.576 sigma threshold, which represents a 99% confidence level. This study highlights the importance of accurate stochastic modeling in ensuring the precision and reliability of the estimated VLBI IVP and antenna axis offset. Additionally, the results can serve as a priori information for VLBI data analysis. |
| format | Article |
| id | doaj-art-6753724cb5184c0ba97e848161f77d1b |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-6753724cb5184c0ba97e848161f77d1b2025-01-10T13:20:02ZengMDPI AGRemote Sensing2072-42922024-12-011714310.3390/rs17010043Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset AnalysisChang-Ki Hong0Tae-Suk Bae1Department Geoinformatics Engineering, Kyungil University, Gyeongsan 38428, Republic of KoreaDepartment Geoinformation Engineering, Sejong University, Seoul 05006, Republic of KoreaThe accuracy and stability of Very Long Baseline Interferometry (VLBI) systems are essential for maintaining global geodetic reference frames such as the International Terrestrial Reference Frame (ITRF). This study focuses on the precise determination of the VLBI Invariant Point (IVP) and the detection of antenna axis offset. Ground-based surveys were conducted at the Sejong Space Geodetic Observatory using high-precision instruments, including total station, to measure slant distances, as well as horizontal and vertical angles from fixed pillars to reflectors attached to the VLBI instrument. The reflectors comprised both prisms and reflective sheets to enhance redundancy and data reliability. A detailed stochastic model incorporating variance component estimation was employed to manage the varying precision of the observations. The analysis revealed significant measurement variability, particularly in slant distance measurements involving prisms. Iterative refinement of the variance components improved the reliability of the IVP and antenna axis offset estimates. The study identified an antenna axis offset of 5.6 mm, which was statistically validated through hypothesis testing, confirming its significance at a 0.01 significance level. This is a significance level corresponding to approximately a 2.576 sigma threshold, which represents a 99% confidence level. This study highlights the importance of accurate stochastic modeling in ensuring the precision and reliability of the estimated VLBI IVP and antenna axis offset. Additionally, the results can serve as a priori information for VLBI data analysis.https://www.mdpi.com/2072-4292/17/1/43stochastic modelantenna axis offsetvariance componentVery Long Baseline Interferometry (VLBI)Invariant Point (IVP) |
| spellingShingle | Chang-Ki Hong Tae-Suk Bae Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis Remote Sensing stochastic model antenna axis offset variance component Very Long Baseline Interferometry (VLBI) Invariant Point (IVP) |
| title | Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis |
| title_full | Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis |
| title_fullStr | Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis |
| title_full_unstemmed | Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis |
| title_short | Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis |
| title_sort | enhanced stochastic models for vlbi invariant point estimation and axis offset analysis |
| topic | stochastic model antenna axis offset variance component Very Long Baseline Interferometry (VLBI) Invariant Point (IVP) |
| url | https://www.mdpi.com/2072-4292/17/1/43 |
| work_keys_str_mv | AT changkihong enhancedstochasticmodelsforvlbiinvariantpointestimationandaxisoffsetanalysis AT taesukbae enhancedstochasticmodelsforvlbiinvariantpointestimationandaxisoffsetanalysis |