Results 1 to 10 of about 88 (75)

Inter-Comparison of UT1-UTC from 24-Hour, Intensives, and VGOS Sessions during CONT17 [PDF]

open access: yesSensors, 2022
This work focuses on the assessment of UT1-UTC estimates from various types of sessions during the CONT17 campaign. We chose the CONT17 campaign as it provides 15 days of continuous, high-quality VLBI data from two legacy networks (S/X band), i.e ...
Harald Schuh   +2 more
exaly   +4 more sources

A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC [PDF]

open access: yesSensors
Accurate ultra-short-term prediction of UT1-UTC is crucial for real-time applications in high-precision reference frame conversions. Presently, traditional LS + AR and LS + MAR hybrid methods are commonly employed for UT1-UTC prediction.
Minsi Ao
exaly   +4 more sources

Probing a southern hemisphere VLBI Intensive baseline configuration for UT1 determination [PDF]

open access: yesEarth, Planets and Space, 2022
The deviation of Universal Time from atomic time, expressed as UT1−UTC, reflects the irregularities of the Earth rotation speed and is key to precise geodetic applications which depend on the transformation between celestial and terrestrial reference ...
Sigrid Böhm   +8 more
doaj   +2 more sources

Research on UT1-UTC and LOD Prediction Algorithm Based on Denoised EAM Dataset

open access: yesRemote Sensing, 2023
The components of EAM are strongly correlated with LOD and play an important role in UT1-UTC and LOD prediction. However, the EAM dataset is prone to be noisy.
Yuanwei Wu, Xishun Li
exaly   +3 more sources

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

open access: yesRemote Sensing, 2020
Errors in ultra-rapid UT1-UTC primarily affect the overall rotation of spatial datum expressed by GNSS (Global Navigation Satellite System) satellite ultra-rapid orbit. In terms of existing errors of traditional strategy, e.g., piecewise linear functions,
Fei Ye, Zhiguo Deng, Yuan Yunbin
exaly   +3 more sources

Middle- and Long-Term UT1-UTC Prediction Based on Constrained Polynomial Curve Fitting, Weighted Least Squares and Autoregressive Combination Model

open access: yesRemote Sensing, 2022
Universal time (UT1-UTC) is a key component of Earth orientation parameters (EOP), which is important for the study of monitoring the changes in the Earth’s rotation rate, climatic variation, and the characteristics of the Earth.
Tianhe Xu, Wenfeng Nie, Yuguo Yang
exaly   +3 more sources

High Frequency Variations of Earth Rotation Parameters from GPS and GLONASS Observations [PDF]

open access: yesSensors, 2015
The Earth’s rotation undergoes changes with the influence of geophysical factors, such as Earth’s surface fluid mass redistribution of the atmosphere, ocean and hydrology.
Erhu Wei   +5 more
doaj   +2 more sources

Accuracy Analysis of SINS/CNS Integrated Attitude Determination Based on Simplified Spatio-Temporal Model [PDF]

open access: yesSensors
For ground-based Celestial Navigation System/Strapdown Inertial Navigation System (CNS/SINS) integrated navigation with arcsecond-level accuracy, the current spatio-temporal transformation model involves a considerable amount of astronomical knowledge ...
Conghai Ruan   +4 more
doaj   +2 more sources

Improved LS+MAR hybrid method to UT1-UTC ultra-short-term prediction by using first-order-difference UT1-UTC

open access: yesGeodesy and Geodynamics
Accurate ultra-short-term prediction of the Earth rotation parameters (ERP) holds paramount importance for real-time applications, particularly in reference frame conversion.
Fei Ye, Yunbin Yuan
exaly   +3 more sources

Improved LOD and UT1-UTC Prediction Using Least Squares Combined with Polynomial CURVE Fitting

open access: yesRemote Sensing
The Length of Day (LOD) and the Universal Time (UT1) play crucial roles in satellite positioning, deep space exploration, and related fields. The primary method for predicting LOD and UT1 is least squares fitting combined with autoregressive (AR) models.
Xuhai Yang, Yuanwei Wu, Xishun Li
exaly   +3 more sources

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