Results 91 to 100 of about 145 (102)

Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

Advances in Space Research, 2017
Abstract UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning.
Song Jia, Tian-he Xu, Zhang-zhen Sun, Jia-jing Li   +3 more
exaly   +2 more sources

Prediction of UT1–UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods

Journal of Geodesy, 2007
This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS ...
Tomasz Niedzielski, Niedzielski Tomasz
exaly   +2 more sources

Estimation of the subdiurnal UT1-UTC variations by the least squares collocation method

Astronomical and Astrophysical Transactions, 2000
Abstract The result of the two-week VLBI experiment CONT'94 has been processed by the least squares collocation method to calculate the UT1-UTC time series with high temporal resolution (one value every few minutes). It allows us to estimate the influence of tidal variations arising from the world's oceans on the EOP.
O A Titov
exaly   +2 more sources

Prediction of UT1-UTC Based on Combination of Weighted Least-Squares and Multivariate Autoregressive

Lecture Notes in Electrical Engineering, 2013
High accurate prediction of UT1-UTC is very important for high-precision aircraft navigation and positioning. In this paper, the weighted least-squares (WLS) combined with multivariate autoregressive (MAR) is proposed to predict UT1-UTC with different span.
Zhang-zhen Sun, Tian-he Xu
exaly   +2 more sources

Prediction of UT1-UTC by machine learning techniques

2022
<p>Machine Learning (ML) algorithms are used to learn from data and make data-driven predictions. These algorithms consider pattern recognition and computational learning on the data. Earth Orientation Parameters (EOP) are the monitoring parameters for the Earth’s rotation.
Sujata Dhar, Shashank Pathak, Robert Heinkelmann, Santiago Belda, Harald Schuh, Nagarajan Balasubramanian   +5 more
openaire   +3 more sources

Impact of the Source Selection and Scheduling Optimization on the Estimation of UT1-UTC in VLBI Intensive Sessions

2023
With the help of Very Long Baseline Interferometry (VLBI), it is possible to determine a large number of parameters, including station and source coordinates as well as the Earth orientation parameters (EOP). Due to the limitation of observations of one hour single baseline sessions, so-called Intensive sessions, only a few parameters such as clock ...
Kern, Lisa Maria, Schartner, Matthias, Böhm, Johannes, Böhm, Sigrid, Nothnagel, Axel Gerhard, Soja, Benedikt   +5 more
openaire   +1 more source

Revisiting European VLBI Intensives for Rapid UT1-UTC Determination

Accurate, low-latency UT1–UTC estimates are essential for monitoring Earth’s highly variable rotation and for real-time applications ranging from GNSS to lunar and deep-space missions. One-hour VLBI Intensive sessions, typically conducted with two stations on long east–west baselines, have the primary goal of providing rapid UT1–UTC estimates.
Lisa Kern, Sigrid Böhm, Johannes Böhm, Sara Bruni, Michiel Otten, Erik Schönemann   +5 more
openaire   +1 more source

Prediction Analysis of UT1-UTC Time Series by Combination of the Least-Squares and Multivariate Autoregressive Method

2011
The objective of this paper is to extensively discuss the theory behind the multivariate autoregressive prediction technique used elsewhere for forecasting Universal Time (UT1-UTC) and to characterise its performance depending on input geodetic and geophysical data.
Tomasz Niedzielski, Wiesław Kosek
openaire   +1 more source

Dynamic Mode Decomposition-based short-term prediction of UT1-UTC and LOD using Atmospheric Angular Momentum time series.

We present preliminary results of ultra-short-term prediction (10-day forecast horizon) of UT1-UTC and LOD. Forecast procedure is based on Dynamic Mode Decomposition (DMD) and uses IERS EOP 14 C04 as a reference as well as Atmospheric Angular Momentum (AAM) as auxiliary data (AAM come from GFZ Potsdam and ETH Zurich).Two main prediction experiments ...
Maciej Michalczak, Marcin Ligas, Henryk Dobslaw, Robert Dill   +3 more
openaire   +1 more source

Prediction Analysis of UT1-UTC Time Series by Combination of the Least-Squares and Multivariate Autoregressive Method

International Association of Geodesy Symposia, 2012
Tomasz Niedzielski, Niedzielski Tomasz
exaly