Browsing by Subject "Sampling patterns"
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Item Open Access Synthetic TEC mapping with kriging and random field priors(IEEE, 2007) Sayın, I.; Arıkan, F.; Arıkan, OrhanTotal Electron Content (TEC) can be used for analyzing the variability of the ionosphere in space and time. In this study, spatial interpolation is implemented by Kriging and Random Field Priors (RFP), which are widely used in geostatistics. Performance of Kriging and RFP methods are analyzed on synthetic TEC data for different trend functions, sampling patterns, sampling numbers, variance and range values of covariance function which is used to simulate the synthetic data, by comparing the normalized errors of interpolations. In regular sampling patterns, as opposed to random sampling, the normalized average error is very close to each other for all methods and trend assumptions. The error increases with variance and decreases with range. As the number of samples increase, the normalized error also decreases.Item Open Access Synthetic TEC mapping with ordinary and universal kriging(IEEE, 2007-06) Sayın, I.; Arıkan, F.; Arıkan, OrhanSpatiotemporal variations in the ionosphere affects the HF and satellite communications and navigation systems. Total Electron Content (TEC) is an important parameter since it can be used to analyze the spatial and temporal variability of the ionosphere. In this study, the performance of the two widely used Kriging algorithms, namely Ordinary Kriging (OrK) and Universal Kriging (UnK), is compared over the synthetic data set. In order to represent various ionospheric states, such as quiet and disturbed days, spatially correlated residual synthetic TEC data with different variances is generated and added to trend functions. Synthetic data sampled with various type of sampling patterns and for a wide range of sampling point numbers. It is observed that for small sampling numbers and with higher variability, OrK gives smaller errors. As the sample number increases, UnK errors decrease faster. For smaller variances in the synthetic surfaces, UnK gives better results. For increasing variance and decreasing range values, usually, the errors increase for both OrK and UnK. © 2007 IEEE.