GNSS-TS-NRS: An Open-Source MATLAB-Based GNSS Time Series Noise Reduction Software

Abstract

The global navigation satellite system (GNSS) has seen tremendous advances in measurement precision and accuracy, and it allows researchers to perform geodynamics and geophysics studies through the analysis of GNSS time series. Moreover, GNSS time series not only contain geophysical signals, but also unmodeled errors and other nuisance parameters, which affect the performance in the estimation of site coordinates and related parameters. As the number of globally distributed GNSS reference stations increases, GNSS time series analysis software should be developed with more flexible format support, better human–machine interaction, and with powerful noise reduction analysis. To meet this requirement, a new software named GNSS time series noise reduction software (GNSS-TS-NRS) was written in MATLAB and was developed. GNSS-TS-NRS allows users to perform noise reduction analysis and spatial filtering on common mode errors and to visualize GNSS position time series. The functions’ related theoretical background of GNSS-TS-NRS were introduced. Firstly, we showed the theoretical background algorithms of the noise reduction analysis (empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD)). We also developed three improved algorithms based on EMD for noise reduction, and the results of the test example showed our proposed methods with better effect. Secondly, the spatial filtering model supported five algorithms on a separate common model error: The stacking filter method, weighted stacking filter method, correlation weighted superposition filtering method, distance weighted filtering method, and principal component analysis, as well as with batch processing. Finally, the developed software also enabled other functions, including outlier detection, correlation coefficient calculation, spectrum analysis, and distribution estimation. The main goal of the manuscript is to share the software with the scientific community to introduce new users to the GNSS time series noise reduction and application.

Drought‐Induced Vertical Displacements and Water Loss in the Po River Basin (Northern Italy) From GNSS Measurements

“…The trend filtering on the time series is performed using the L1tool software (https://github.com/wudingcheng/l1tool). The stacking of the GNSS time series is performed using the GNSS_TS_ NRS code (https://github.com/CL-Xiong/GNSS-TS-NRS, He et al, 2020). GLDAS data was downloaded from https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_2.1/summary?keywords=GLDAS (Beaudoing and Rodell, 2020) and available at https://doi.org/10.6084/m9.figshare.24551167.v1 for this study region (GLDAS_ NOAH025_until202303daily.nc4).…”

GNSS Data Processing and Validation of the Altimeter Zenith Wet Delay around the Wanshan Calibration Site

“…The IQR-Factor 2.2 was used to obtain the solutions of the N, E, and U directions of GAMIT/GLOBK. The following relation is used in this research [28]:…”

GNSS Imaging of Strain Rate Changes and Vertical Crustal Motions over the Tibetan Plateau

“…Due to the impact of several strong earthquakes, some sites have recorded significant coseismic and postseismic deformations. Considering this problem, we model the position time series of continuous GNSS sites according to the modeling scheme described in Xiang et al [25], which can simultaneously extract and model the linear, annual, semiannual, coseismic, and postseismic signals [26][27][28]. Figure 2 displays the modeled signals of GNSS position time series for sites XZAR and XZZF.…”