Recent Advances in the Geodesy Data Processing
Geodetic functional models, stochastic models, and model parameter estimation theory are fundamental for geodetic data processing. In the past five years, through the unremitting efforts of Chinese scholars in the field of geodetic data processing, according to the application and practice of geodes...
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| Format: | Article |
| Language: | English |
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Surveying and Mapping Press
2023-09-01
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| Series: | Journal of Geodesy and Geoinformation Science |
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| Online Access: | http://jggs.chinasmp.com/fileup/2096-5990/PDF/1698731793203-33377147.pdf |
| _version_ | 1797644393595797504 |
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| author | Jianjun ZHU, Leyang WANG, Jun HU, Bofeng LI, Haiqiang FU, Yibin YAO |
| author_facet | Jianjun ZHU, Leyang WANG, Jun HU, Bofeng LI, Haiqiang FU, Yibin YAO |
| author_sort | Jianjun ZHU, Leyang WANG, Jun HU, Bofeng LI, Haiqiang FU, Yibin YAO |
| collection | DOAJ |
| description | Geodetic functional models, stochastic models, and model parameter estimation theory are fundamental for geodetic data processing. In the past five years, through the unremitting efforts of Chinese scholars in the field of geodetic data processing, according to the application and practice of geodesy, they have made significant contributions in the fields of hypothesis testing theory, un-modeled error, outlier detection, and robust estimation, variance component estimation, complex least squares, and ill-posed problems treatment. Many functional models such as the nonlinear adjustment model, EIV model, and mixed additive and multiplicative random error model are also constructed and improved. Geodetic data inversion is an important part of geodetic data processing, and Chinese scholars have done a lot of work in geodetic data inversion in the past five years, such as seismic slide distribution inversion, intelligent inversion algorithm, multi-source data joint inversion, water reserve change and satellite gravity inversion. This paper introduces the achievements of Chinese scholars in the field of geodetic data processing in the past five years, analyzes the methods used by scholars and the problems solved, and looks forward to the unsolved problems in geodetic data processing and the direction that needs further research in the future. |
| first_indexed | 2024-03-11T14:29:52Z |
| format | Article |
| id | doaj.art-1d883f3f3fbe47edaf22316d49a4887a |
| institution | Directory Open Access Journal |
| issn | 2096-5990 |
| language | English |
| last_indexed | 2024-03-11T14:29:52Z |
| publishDate | 2023-09-01 |
| publisher | Surveying and Mapping Press |
| record_format | Article |
| series | Journal of Geodesy and Geoinformation Science |
| spelling | doaj.art-1d883f3f3fbe47edaf22316d49a4887a2023-10-31T10:00:21ZengSurveying and Mapping PressJournal of Geodesy and Geoinformation Science2096-59902023-09-0163334510.11947/j.JGGS.2023.0304Recent Advances in the Geodesy Data ProcessingJianjun ZHU, Leyang WANG, Jun HU, Bofeng LI, Haiqiang FU, Yibin YAO01. School of Geosciences and Info-physics, Central South University, Changsha 410012, China;2. School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330000, China;3. College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China;4. School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, ChinaGeodetic functional models, stochastic models, and model parameter estimation theory are fundamental for geodetic data processing. In the past five years, through the unremitting efforts of Chinese scholars in the field of geodetic data processing, according to the application and practice of geodesy, they have made significant contributions in the fields of hypothesis testing theory, un-modeled error, outlier detection, and robust estimation, variance component estimation, complex least squares, and ill-posed problems treatment. Many functional models such as the nonlinear adjustment model, EIV model, and mixed additive and multiplicative random error model are also constructed and improved. Geodetic data inversion is an important part of geodetic data processing, and Chinese scholars have done a lot of work in geodetic data inversion in the past five years, such as seismic slide distribution inversion, intelligent inversion algorithm, multi-source data joint inversion, water reserve change and satellite gravity inversion. This paper introduces the achievements of Chinese scholars in the field of geodetic data processing in the past five years, analyzes the methods used by scholars and the problems solved, and looks forward to the unsolved problems in geodetic data processing and the direction that needs further research in the future.http://jggs.chinasmp.com/fileup/2096-5990/PDF/1698731793203-33377147.pdf|stochastic model|functional model|robust estimation|variance component estimation|geodetic data inversion |
| spellingShingle | Jianjun ZHU, Leyang WANG, Jun HU, Bofeng LI, Haiqiang FU, Yibin YAO Recent Advances in the Geodesy Data Processing Journal of Geodesy and Geoinformation Science |stochastic model|functional model|robust estimation|variance component estimation|geodetic data inversion |
| title | Recent Advances in the Geodesy Data Processing |
| title_full | Recent Advances in the Geodesy Data Processing |
| title_fullStr | Recent Advances in the Geodesy Data Processing |
| title_full_unstemmed | Recent Advances in the Geodesy Data Processing |
| title_short | Recent Advances in the Geodesy Data Processing |
| title_sort | recent advances in the geodesy data processing |
| topic | |stochastic model|functional model|robust estimation|variance component estimation|geodetic data inversion |
| url | http://jggs.chinasmp.com/fileup/2096-5990/PDF/1698731793203-33377147.pdf |
| work_keys_str_mv | AT jianjunzhuleyangwangjunhubofenglihaiqiangfuyibinyao recentadvancesinthegeodesydataprocessing |