Volume 15, Issue 4

The ionospheric total electron content (TEC) severely impacts the positional accuracy of a single frequency Global Positioning System (GPS) receiver at the equatorial latitudes. The ionosphere causes a frequency-dependent group delay in the GPS-ranging signals, which reduces the receiver’s accuracy. Further, the variations in TEC due to various space weather phenomena make the ionosphere’s behaviour nonhomogeneous and complex. Hence, developing an accurate forecast model that can track the dynamic behaviour of the ionosphere remains a challenge. However, advances in emerging data-driven algorithms have been found helpful in tracking non-stationary behavior in TEC. These models help forecast the delays in advance. The multivariate Vector Autoregression model (VAR) predicts the Ionospheric TEC in the proposed model. The prediction model uses input data compiled in real-time from the lag values of incoming TEC data and features extracted from TEC. The TEC is predicted in real-time and tested for different prediction intervals. The metrics – Mean Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used for testing and validating the accuracy of the model statistically. Testing the predicted output accuracy is also done with the dynamic time warping (DTW) algorithm by comparing it with the actual value obtained from the dual-frequency receiver. The model is tested for storm days of the year 2015 for Bangalore and Hyderabad stations and found to be reliable and accurate. A prediction interval of twenty-minute shows the highest accuracy with an error within 10 TECU for all the storm days.

The objective of this study is to investigate the effect of the Baltic Sea non-tidal loading in the territory of Latvia using observations of the GNSS continuously operating reference stations (CORS) of LatPos, EUPOS ® -Riga, EPN and EstPos networks. The GNSS station daily coordinate time series obtained in a double-difference (DD) mode were used. For representation of the sea level dynamics, the Latvian tide gauge records were used. Performed correlation analysis is based on yearly data sets of these observations for the period from 2012 up to 2020. The approach discloses how the non-tidal loading can induce variations in the time series of the regional GNSS station network. This paper increases understanding of the Earth’s surface displacements occurring due to the non-tidal loading effect in Latvia, and is intended to raise the importance and necessity of improved Latvian GNSS time series by removing loading effects.

Suppose a large and dense point cloud of an object with complex geometry is available that can be approximated by a smooth univariate function. In general, for such point clouds the “best” approximation using the method of least squares is usually hard or sometimes even impossible to compute. In most cases, however, a “near-best” approximation is just as good as the “best”, but usually much easier and faster to calculate. Therefore, a fast approach for the approximation of point clouds using Chebyshev polynomials is described, which is based on an interpolation in the Chebyshev points of the second kind. This allows to calculate the unknown coefficients of the polynomial by means of the Fast Fourier transform (FFT), which can be extremely efficient, especially for high-order polynomials. Thus, the focus of the presented approach is not on sparse point clouds or point clouds which can be approximated by functions with few parameters, but rather on large dense point clouds for whose approximation perhaps even millions of unknown coefficients have to be determined.

Since the development of GNSS techniques, the determination of a precise quasi-geoid model has become even more actual. In terms of this project the staff of the Institute of Geodesy and Geoinformatics (GGI) has developed a new quasi-geoid model based on DFHRS (Digital Finite-element Height Reference Surface) approach additionally using astrogeodetic measurements – vertical deflections (VD), which can be observed by a Digital zenith camera. This paper evaluates a quasi-geoid model results based on vertical deflections, as a study area using the territory of Latvia: the standard deviation of the solution is equal to 0.006 m with observation residuals after the adjustment of minimum and maximum differences −0.012 and 0.012 accordingly. The standard deviation of quasi-geoid heights and h-H values from LGIA database is equal to 0.012 m with minimum and maximum differences −0.026 and 0.025 accordingly. The post-processed terrestrial VD observations have been compared to VD derivatives from EGM2008 and GGMplus geopotential models. The developed quasi-geoid has been compared to the national quasi-geoid model LV’14 and to the Scandinavian NKG2015.