On the Handling of Outliers in the GNSS Time Series by Means of the Noise and Probability Analysis

摘要: The data pre-analysis plays a significant role in the noise determination. most important issue is to find an optimum criterion for outliers removal, since their existence can affect any further analysis. noises GNSS time series are characterized by spectral index and amplitudes that be determined with few different methods. In this research, Maximum Likelihood Estimation (MLE) was used. as well indices were obtained topocentric coordinates daily changes from selected EPN (EUREF Permanent Network) stations. within re-processing made Military University of Technology Local Analysis Centre (MUT LAC). removed noisy 12 stations criteria 3 5 times standard deviations (3σ, 5σ) Median Absolute Deviation (MAD) investigate how they parameters. results show removal necessary before analysis, otherwise one may obtain quite odd unrealistic values. probability analysis skewness kurtosis also performed beyond values assuming wrong leads case distribution. On basis results, we propose use MAD method series.