Open AccessA New Statistical Test Based on the WR for Detecting Offsets in GPS Experiment
Author(s) -
Tehranchi Ramin,
MoghtasedAzar Khosro,
Safari Abdolreza
Publication year - 2020
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2019ea000810
Subject(s) - global positioning system , wavelet , computer science , algorithm , series (stratigraphy) , preprocessor , mathematics , artificial intelligence , geology , telecommunications , paleontology
Detecting the probable offsets is a crucial step in the preprocessing of the Global Positioning System (GPS) coordinate time series. Undetected offsets lead to the biased estimation of time series parameters and their uncertainties resulting in data misinterpretation. In the current research, a DIA (detection, identification, and adaptation)‐based procedure in maximal overlap discrete wavelet transform (MODWT) rough space has been introduced to address the location of offsets in long GPS time series without a priori information of the functional or stochastic models. A remarkable property of a wavelet rough (WR) at lower‐scale ( j ≤ 5 ) details is to reflect the local regularity of the time series, being large where the signal is irregular and small where it is smooth. Performance and effectiveness of the proposed approach have been shown with DOGEx (Detection of Offsets in GPS Experiment) data set, which was a simulated time series that mimicked realistic GPS data consisting of a velocity component, seasonal cycle, offsets, and white and flicker noises composed in an additive model. The results showed that the fifth percentile range (5% to 95%) in velocity biases was equal to 1.24 mm/yr, which was smaller than all tested automatic solutions. Furthermore, the offsets detected by this method were split into 34.3% of true positive (TP), 36.5% of false positive (FP), and 29.2% of the false negative (FN), offering the proposed method as the best automatic solution.