Stochastic Models for Geodesy and Geoinformation Science

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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Bibliographic Details
Other Authors: Neitzel, Frank (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
History of engineering & technology
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
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