Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert 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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/10/1584 |
| _version_ | 1819002039822188544 |
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| author | Guo Chen Qile Zhao Na Wei Jingnan Liu |
| author_facet | Guo Chen Qile Zhao Na Wei Jingnan Liu |
| author_sort | Guo Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T22:58:46Z |
| format | Article |
| id | doaj.art-7c91e8a0dc0c46b5ad7a217428491301 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T22:58:46Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7c91e8a0dc0c46b5ad7a2174284913012022-12-21T19:24:03ZengMDPI AGRemote Sensing2072-42922018-10-011010158410.3390/rs10101584rs10101584Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation ParametersGuo Chen0Qile Zhao1Na Wei2Jingnan Liu3School of Geodesy and Geomatics, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, ChinaThe 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.http://www.mdpi.com/2072-4292/10/10/1584seasonal signalweight matrixoffsettransformation parametersnoise analyses |
| spellingShingle | Guo Chen Qile Zhao Na Wei Jingnan Liu Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters Remote Sensing seasonal signal weight matrix offset transformation parameters noise analyses |
| title | Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters |
| title_full | Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters |
| title_fullStr | Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters |
| title_full_unstemmed | Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters |
| title_short | Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters |
| title_sort | impacts on noise analyses of gnss position time series caused by seasonal signal weight matrix offset and helmert transformation parameters |
| topic | seasonal signal weight matrix offset transformation parameters noise analyses |
| url | http://www.mdpi.com/2072-4292/10/10/1584 |
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