scholarly journals Effect of Helmert Transformation Parameters and Weight Matrix on Seasonal Signals in GNSS Coordinate Time Series

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2127
Author(s):  
Guo Chen ◽  
Qile Zhao ◽  
Na Wei ◽  
Min Li
Keyword(s):  
Time Series ◽  
Weight Matrix ◽  
Helmert Transformation ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Seasonal Signals ◽  
Transformation Parameters

scholarly journals Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters

2018 ◽  
Vol 10 (10) ◽  
pp. 1584 ◽  
Author(s):  
Guo Chen ◽  
Qile Zhao ◽  
Na Wei ◽  
Jingnan Liu
Keyword(s):  
Time Series ◽  
Colored Noise ◽  
Deterministic Model ◽  
Simulated Data ◽  
Weight Matrix ◽  
Helmert Transformation ◽  
Seasonal Signals ◽  
Position Time Series ◽  
Power Law Noise ◽  
Transformation Parameters

The noise characteristics of the Global Navigation Satellite System (GNSS) position time series can be biased by many factors, which in turn affect the estimates of parameters in the deterministic model using a least squares method. The authors assess the effects of seasonal signals, weight matrix, intermittent offsets, and Helmert transformation parameters on the noise analyses. Different solutions are obtained using the simulated and real position time series of 647 global stations and power law noise derived from the residuals of stacking solutions are compared. Since the true noise in the position time series is not available except for the simulated data, the authors paid most attention to the noise difference caused by the variable factors. First, parameterization of seasonal signals in the time series can reduce the colored noise and cause the spectral indexes to be closer to zero (much “whiter”). Meanwhile, the additional offset parameters can also change the colored noise to be much “whiter” and more offsets parameters in the deterministic model leading to spectral indexes closer to zero. Second, the weight matrices derived from the covariance information can induce more colored noise than the unit weight matrix for both real and simulated data, and larger biases of annual amplitude of simulated data are attributed to the covariance information. Third, the Helmert transformation parameters (three translation, three rotation, and one scale) considered in the model show the largest impacts on the power law noise (medians of 0.4 mm−k/4 and 0.06 for the amplitude and spectral index, respectively). Finally, the transformation parameters and full-weight matrix used together in the stacking model can induce different patterns for the horizontal and vertical components, respectively, which are related to different dominant factors.

scholarly journals Extracting Seasonal Signals in GNSS Coordinate Time Series via Weighted Nuclear Norm Minimization

2020 ◽  
Vol 12 (12) ◽  
pp. 2027
Author(s):  
Baozhou Chen ◽  
Jiawen Bian ◽  
Kaihua Ding ◽  
Haochen Wu ◽  
Hongwei Li
Keyword(s):  
Time Series ◽  
Hankel Matrix ◽  
Nuclear Norm ◽  
Low Rank ◽  
Nuclear Norm Minimization ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Norm Minimization ◽  
Seasonal Signals ◽  
Weighted Nuclear Norm Minimization

Global Navigation Satellite System (GNSS) coordinate time series contains obvious seasonal signals, which mainly manifest as a superposition of annual and semi-annual oscillations. Accurate extraction of seasonal signals is of great importance for understanding various geophysical phenomena. In this paper, a Weighted Nuclear Norm Minimization (WNNM) is proposed to extract the seasonal signals from the GNSS coordinate time series. WNNM assigns different weights to different singular values that enable us to estimate an approximate low rank matrix from its noisy matrix. To address this issue, the low rank characteristics of the Hankel matrix induced by GNSS coordinate time series was investigated first, and then the WNNM is applied to extract the seasonal signals in the GNSS coordinate time series. Meanwhile, the residuals have been analyzed, obtaining the estimation of the uncertainty of velocity. To demonstrate the effectiveness of the proposed algorithm, a number of tests have been carried out on both simulated and real GNSS dataset. Experimental results indicate that the proposed scheme offers preferable performances compared with many state-of-the-art methods.

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

Spectral analysis for GNSS coordinate time series using chirp Fourier transform

2017 ◽  
Vol 65 (6) ◽  
pp. 1111-1118
Author(s):  
Shengtao Feng ◽  
Wanju Bo ◽  
Qingzun Ma ◽  
Zifan Wang
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Fourier Transform ◽  
Coordinate Time ◽  
Coordinate Time Series

scholarly journals A Multiple Algorithm Approach to the Analysis of GNSS Coordinate Time Series for Detecting Geohazards and Anomalies

2020 ◽  
Vol 125 (2) ◽  
Author(s):  
Mohammed Habboub ◽  
Panos A. Psimoulis ◽  
Richard Bingley ◽  
Markus Rothacher
Keyword(s):  
Time Series ◽  
Coordinate Time ◽  
Coordinate Time Series
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