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A new statistical test based on the Wavelet Rough (WR) for detecting offsets in GPS experiment
Earth and Space Science ( IF 2.312 ) Pub Date : 2020-07-31 , DOI: 10.1029/2019ea000810
Ramin Tehranchi; Khosro Moghtased‐Azar; Abdolreza Safari

Detecting the probable offsets is a crucial step in the pre‐processing 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, 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 apriori information of the functional or stochastic models. A remarkable property of a wavelet rough 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, white and flicker noises composed in an additive model. The results showed that the fifth percentile range (5% to 9%) in velocity biases was equal to 1.24 mm/year, 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.
更新日期:2020-08-01

 

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