A Wavelet-Based Outlier Detection and Noise Component Analysis for GNSS Position Time Series Article Swipe
Kunpu Ji
,
Yunzhong Shen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/1345_2020_106
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/1345_2020_106
Various signals of crustal deformation and mass loading deformation are contained in a GNSS position time series. However, a GNSS position time series is also polluted by outliers and various colored noise, which must be reasonably modelled before estimating deformation signals. Since temporal signals of the GNSS position time series are non-linear and complicated, we propose a wavelet-based approach for outlier detection, which first retrieves the temporal signals from the GNSS position time series by using wavelet analysis, and then detect outliers in the residual position time series by using the interquartile range. After the detected outliers are eliminated from the residual time series, the noise components, including white noise and flicker noise, are estimated by using MINQUE approach. Our proposed approach is used to process the real GNSS position time series of the Crustal Movement Observation Network of China (CMONOC) over the period spanning 1999–2018. The results demonstrate that our approach can detect the outliers more efficiently than the traditional approach, which retrieves the temporal signals by using a functional model with trend and periodic variations. As a result, the noise components estimated with our proposed approach are smaller than those with the traditional approach for the GNSS position time series of all CMONOC stations.
Related Topics
Concepts
GNSS applications
Wavelet
Noise (video)
Position (finance)
Outlier
Time series
Computer science
Wavelet transform
Geodesy
Residual
Series (stratigraphy)
White noise
Artificial intelligence
Algorithm
Geography
Geology
Global Positioning System
Telecommunications
Machine learning
Economics
Finance
Paleontology
Image (mathematics)
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1007/1345_2020_106
- https://link.springer.com/content/pdf/10.1007%2F1345_2020_106.pdf
- OA Status
- hybrid
- Cited By
- 4
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3025877172
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3025877172Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/1345_2020_106Digital Object Identifier
- Title
-
A Wavelet-Based Outlier Detection and Noise Component Analysis for GNSS Position Time SeriesWork title
- Type
-
book-chapterOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Kunpu Ji, Yunzhong ShenList of authors in order
- Landing page
-
https://doi.org/10.1007/1345_2020_106Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007%2F1345_2020_106.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007%2F1345_2020_106.pdfDirect OA link when available
- Concepts
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GNSS applications, Wavelet, Noise (video), Position (finance), Outlier, Time series, Computer science, Wavelet transform, Geodesy, Residual, Series (stratigraphy), White noise, Artificial intelligence, Algorithm, Geography, Geology, Global Positioning System, Telecommunications, Machine learning, Economics, Finance, Paleontology, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2137995611, https://openalex.org/W2017104286, https://openalex.org/W1997722977, https://openalex.org/W4255272544, https://openalex.org/W2158513598, https://openalex.org/W2769025865, https://openalex.org/W1547669723, https://openalex.org/W2795079625, https://openalex.org/W2080702225, https://openalex.org/W2146799928, https://openalex.org/W1562504550, https://openalex.org/W1982387893, https://openalex.org/W2061605459, https://openalex.org/W2151501940, https://openalex.org/W2008955135, https://openalex.org/W2295908720, https://openalex.org/W2023728704, https://openalex.org/W2618771825, https://openalex.org/W2058597816, https://openalex.org/W114250020, https://openalex.org/W118945080, https://openalex.org/W1572472014, https://openalex.org/W2165996527, https://openalex.org/W2062024414, https://openalex.org/W2339609730, https://openalex.org/W2027515500, https://openalex.org/W1990044503 |
| referenced_works_count | 27 |
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| abstract_inverted_index.our | 151, 186 |
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| abstract_inverted_index.series. | 17 |
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| abstract_inverted_index.outliers | 28, 82, 97, 156 |
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| abstract_inverted_index.proposed | 121, 187 |
| abstract_inverted_index.residual | 85, 102 |
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| abstract_inverted_index.approach, | 162 |
| abstract_inverted_index.approach. | 119 |
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| abstract_inverted_index.efficiently | 158 |
| abstract_inverted_index.traditional | 161, 195 |
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| abstract_inverted_index.1999–2018. | 146 |
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| citation_normalized_percentile.is_in_top_10_percent | True |