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022 |a 2072-4292 
024 7 |a 10.3390/rs12213532  |2 doi 
035 |a 2550344346 
045 2 |b d20200101  |b d20201231 
084 |a 231556  |2 nlm 
100 1 |a He, Xiaoxing  |u School of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, China; <email>hexiaoxing@whu.edu.cn</email> 
245 1 |a GNSS-TS-NRS: An Open-Source MATLAB-Based GNSS Time Series Noise Reduction Software 
260 |b MDPI AG  |c 2020 
513 |a Journal Article 
520 3 |a 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. 
653 |a Mean square errors 
653 |a Outliers (statistics) 
653 |a Spatial analysis 
653 |a Accuracy 
653 |a Software packages 
653 |a Spatial filtering 
653 |a Noise reduction 
653 |a Algorithms 
653 |a Software 
653 |a Matlab 
653 |a Geophysics 
653 |a Signal processing 
653 |a Spectrum analysis 
653 |a Decomposition 
653 |a Computer simulation 
653 |a Geodynamics 
653 |a Empirical analysis 
653 |a Time series 
653 |a Correlation coefficients 
653 |a Correlation coefficient 
653 |a Batch processing 
653 |a Global positioning systems--GPS 
653 |a Data analysis 
653 |a Background noise 
653 |a Seasonal variations 
653 |a Computer programs 
653 |a Principal components analysis 
653 |a Signal to noise ratio 
653 |a Earthquakes 
653 |a Stacking 
653 |a Parameters 
653 |a Interfaces 
653 |a Global navigation satellite system 
653 |a Open source software 
653 |a Remote sensing 
700 1 |a Yu, Kegen  |u School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China; <email>kgyu@sgg.whu.edu.cn</email> 
700 1 |a Jean-Philippe Montillet  |u Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC), CH-7260 Davos, Switzerland; <email>jean-philippe.montillet@pmodwrc.ch</email> 
700 1 |a Xiong, Changliang  |u School of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, China; <email>hexiaoxing@whu.edu.cn</email>; Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Science (CAS), Wuhan 430077, China 
700 1 |a Lu, Tieding  |u School of Geodesy and Geomatics, East China University of Technology, Nanchang 330013, China; <email>tdlu@whu.edu.cn</email> (T.L.); <email>shjzhou@nchu.edu.cn</email> (S.Z.) 
700 1 |a Zhou, Shijian  |u School of Geodesy and Geomatics, East China University of Technology, Nanchang 330013, China; <email>tdlu@whu.edu.cn</email> (T.L.); <email>shjzhou@nchu.edu.cn</email> (S.Z.); Nanchang Hangkong University, Nanchang 330063, China 
700 1 |a Ma, Xiaping  |u School of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China; <email>xpmaxkd16@xust.edu.cn</email> 
700 1 |a Cui, Hongchao  |u Hubei Land Resources Vocational College, Wuhan 430090, China; <email>hccui@ecut.edu.cn</email> 
700 1 |a Feng, Ming  |u Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China; <email>mf_pla@hotmail.com</email> 
773 0 |t Remote Sensing  |g vol. 12, no. 21 (2020), p. 3532 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2550344346/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/2550344346/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/2550344346/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch