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 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.

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Publication Details

Journal
Remote Sensing
Published
2024-12-26
DOI
https://doi.org/10.3390/rs17010043
Primary Topic
Adaptive optics and wavefront sensing
Type
article
Field-Weighted Citation Impact
0.00

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article

Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis

Tae‐Suk Bae, Chang-Ki Hong
Remote Sensing
Adaptive optics and wavefront sensing
article

Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis

Tae‐Suk Bae, Chang-Ki Hong
article en

Abstract

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.

Remote SensingVol. 17(1)
Sejong University (KR), Kyungil University (KR)
National Geographic Society, National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 78%
Adaptive optics and wavefront sensing
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Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis — Tae‐Suk Bae, Chang-Ki Hong · Remote Sensing (2024) | TGRS Research Map | TGRS