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...

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Year of Publication:2021
Language:English
Physical Description:1 electronic resource (200 p.)
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spelling Neitzel, Frank edt
Stochastic Models for Geodesy and Geoinformation Science
Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
1 electronic resource (200 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
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.
English
History of engineering & technology bicssc
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
3-03943-981-2
3-03943-982-0
Neitzel, Frank oth
language English
format eBook
author2 Neitzel, Frank
author_facet Neitzel, Frank
author2_variant f n fn
author2_role Sonstige
title Stochastic Models for Geodesy and Geoinformation Science
spellingShingle 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_auth Stochastic Models for Geodesy and Geoinformation Science
title_new Stochastic Models for Geodesy and Geoinformation Science
title_sort stochastic models for geodesy and geoinformation science
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2021
physical 1 electronic resource (200 p.)
isbn 3-03943-981-2
3-03943-982-0
illustrated Not Illustrated
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is_hierarchy_title Stochastic Models for Geodesy and Geoinformation Science
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