Stochastic Models for Geodesy and Geoinformation Science
In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and...
Збережено в:
| Формат: | Online |
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| Мова: | Англійська |
| Опубліковано: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Предмети: | |
| Онлайн доступ: | ONIX_20210501_9783039439812_120 |
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| _version_ | 1869527008425279488 |
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| collection | Directory of Open Access Books |
| description | In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena. |
| format | Online |
| id | doab-20.500.12854ir-68374 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-683742024-04-11T15:10:30Z Stochastic Models for Geodesy and Geoinformation Science Neitzel, Frank EM-algorithm multi-GNSS PPP process noise observation covariance matrix extended Kalman filter machine learning GNSS phase bias sequential quasi-Monte Carlo variance reduction autoregressive processes ARMA-process colored noise continuous process covariance function stochastic modeling time series elementary error model terrestrial laser scanning variance-covariance matrix terrestrial laser scanner stochastic model B-spline approximation Hurst exponent fractional Gaussian noise generalized Hurst estimator very long baseline interferometry sensitivity internal reliability robustness CONT14 Errors-In-Variables Model Total Least-Squares prior information collocation vs. adjustment mean shift model variance inflation model outlierdetection likelihood ratio test Monte Carlo integration data snooping GUM analysis geodetic network adjustment stochastic properties random number generator Monte Carlo simulation 3D straight line fitting total least squares (TLS) weighted total least squares (WTLS) nonlinear least squares adjustment direct solution singular dispersion matrix laser scanning data thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena. 2021-05-01T15:08:24Z 2021-05-01T15:08:24Z 2021 book ONIX_20210501_9783039439812_120 9783039439812 9783039439829 https://directory.doabooks.org/handle/20.500.12854/68374 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/3387 https://mdpi.com/books/pdfview/book/3387 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03943-982-9 10.3390/books978-3-03943-982-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039439812 9783039439829 200 Basel, Switzerland open access |
| spellingShingle | EM-algorithm multi-GNSS PPP process noise observation covariance matrix extended Kalman filter machine learning GNSS phase bias sequential quasi-Monte Carlo variance reduction autoregressive processes ARMA-process colored noise continuous process covariance function stochastic modeling time series elementary error model terrestrial laser scanning variance-covariance matrix terrestrial laser scanner stochastic model B-spline approximation Hurst exponent fractional Gaussian noise generalized Hurst estimator very long baseline interferometry sensitivity internal reliability robustness CONT14 Errors-In-Variables Model Total Least-Squares prior information collocation vs. adjustment mean shift model variance inflation model outlierdetection likelihood ratio test Monte Carlo integration data snooping GUM analysis geodetic network adjustment stochastic properties random number generator Monte Carlo simulation 3D straight line fitting total least squares (TLS) weighted total least squares (WTLS) nonlinear least squares adjustment direct solution singular dispersion matrix laser scanning data thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Stochastic Models for Geodesy and Geoinformation Science |
| title | Stochastic Models for Geodesy and Geoinformation Science |
| title_full | Stochastic Models for Geodesy and Geoinformation Science |
| title_fullStr | Stochastic Models for Geodesy and Geoinformation Science |
| title_full_unstemmed | Stochastic Models for Geodesy and Geoinformation Science |
| title_short | Stochastic Models for Geodesy and Geoinformation Science |
| title_sort | stochastic models for geodesy and geoinformation science |
| topic | EM-algorithm multi-GNSS PPP process noise observation covariance matrix extended Kalman filter machine learning GNSS phase bias sequential quasi-Monte Carlo variance reduction autoregressive processes ARMA-process colored noise continuous process covariance function stochastic modeling time series elementary error model terrestrial laser scanning variance-covariance matrix terrestrial laser scanner stochastic model B-spline approximation Hurst exponent fractional Gaussian noise generalized Hurst estimator very long baseline interferometry sensitivity internal reliability robustness CONT14 Errors-In-Variables Model Total Least-Squares prior information collocation vs. adjustment mean shift model variance inflation model outlierdetection likelihood ratio test Monte Carlo integration data snooping GUM analysis geodetic network adjustment stochastic properties random number generator Monte Carlo simulation 3D straight line fitting total least squares (TLS) weighted total least squares (WTLS) nonlinear least squares adjustment direct solution singular dispersion matrix laser scanning data thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | EM-algorithm multi-GNSS PPP process noise observation covariance matrix extended Kalman filter machine learning GNSS phase bias sequential quasi-Monte Carlo variance reduction autoregressive processes ARMA-process colored noise continuous process covariance function stochastic modeling time series elementary error model terrestrial laser scanning variance-covariance matrix terrestrial laser scanner stochastic model B-spline approximation Hurst exponent fractional Gaussian noise generalized Hurst estimator very long baseline interferometry sensitivity internal reliability robustness CONT14 Errors-In-Variables Model Total Least-Squares prior information collocation vs. adjustment mean shift model variance inflation model outlierdetection likelihood ratio test Monte Carlo integration data snooping GUM analysis geodetic network adjustment stochastic properties random number generator Monte Carlo simulation 3D straight line fitting total least squares (TLS) weighted total least squares (WTLS) nonlinear least squares adjustment direct solution singular dispersion matrix laser scanning data thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20210501_9783039439812_120 |