Stochastic Models for Geodesy and Geoinformation Science

• Andere auteurs: Neitzel, Frank (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Onderwerpen: 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 toegang: DOAB: download the publication; DOAB: description of the publication

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...