GNSS Vertical Coordinate Time Series Analysis Using Single-Channel Independent Component Analysis Method

2017 ◽  
pp. 265-278
Author(s):  
Wei Peng ◽  
Wujiao Dai ◽  
Rock Santerre ◽  
Changsheng Cai ◽  
Cuilin Kuang
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Time Series Analysis ◽  
Single Channel ◽  
Independent Component ◽  
Analysis Method ◽  
Vertical Coordinate ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Independent Component Analysis Method

scholarly journals Modeling Seasonal Variations in Vertical GPS Coordinate Time Series Using Independent Component Analysis and Varying Coefficient Regression

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5627
Author(s):  
Bin Liu ◽  
Xuemin Xing ◽  
Jianbo Tan ◽  
Qing Xia
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Seasonal Variations ◽  
Component Analysis ◽  
Independent Component ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Varying Coefficient ◽  
The Common ◽  
Gps Coordinate Time Series

Common seasonal variations in Global Positioning System (GPS) coordinate time series always exist, and the modeling and correction of the seasonal signals are helpful for many geodetic studies using GPS observations. A spatiotemporal model was proposed to model the common seasonal variations in vertical GPS coordinate time series, based on independent component analysis and varying coefficient regression method. In the model, independent component analysis (ICA) is used to separate the common seasonal signals in the vertical GPS coordinate time series. Considering that the periodic signals in GPS coordinate time series change with time, a varying coefficient regression method is used to fit the separated independent components. The spatiotemporal model was then used to fit the vertical GPS coordinate time series of 262 global International GPS Service for Geodynamics (IGS) GPS sites. The results show that compared with least squares regression, the varying coefficient method can achieve a more reliable fitting result for the seasonal variation of the separated independent components. The proposed method can accurately model the common seasonal variations in the vertical GPS coordinate time series, with an average root mean square (RMS) reduction of 41.6% after the model correction.

A Method Based on MTLS and ILSP for GNSS Coordinate Time Series Analysis with Missing Data

2021 ◽  
Author(s):  
Yingying Ren ◽  
Hu Wang ◽  
Lizhen Lian ◽  
Jiexian Wang ◽  
Yingyan Cheng ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Analysis ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Series Analysis

scholarly journals Evaluation of Harmonic Contributions for Multi Harmonic Sources System Based on Mixed Entropy Screening and an Improved Independent Component Analysis Method

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 323
Author(s):  
Jinshuai Zhao ◽  
Honggeng Yang ◽  
Xiaoyang Ma ◽  
Fangwei Xu
Keyword(s):  
Independent Component Analysis ◽  
Global Positioning System ◽  
Component Analysis ◽  
Potential Method ◽  
Independent Component ◽  
Analysis Method ◽  
Data Segment ◽  
Global Positioning ◽  
Calculation Process ◽  
Independent Component Analysis Method

Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.

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