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...

Full description

Saved in:

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
Online Access:	Get Fullteks DOAB: description of the publication
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Stochastic Models for Geodesy and Geoinformation Science

Similar Items