Special Issue "Stochastic Models for Geodesy and Geoinformation Science"
Deadline for manuscript submissions: closed (31 May 2020).
Interests: adjustment theory; terrestrial laser scanning; surface approximation; measurement-and model-based structural analysis
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, for example, the 3D coordinates of an object. Therefore, the unknown parameters must be linked with measured values, for example, directions, angles, and distances, by a mathematical model. This consists of two fundamental components—the functional and the stochastic model. The functional model describes the geometrical–physical relationship between the measured values and the unknown parameters. This relationship is sufficiently well known for most applications.
The definition of a stochastic model in the form of a variance–covariance matrix for the measurements, which best corresponds to the real conditions, is however a big challenge, as influences from the (multi)-sensor system, the signal path, and the object properties have to be considered. With the help of adjustment calculations and variance–covariance propagation, the unknown parameters can be determined and the necessary cofactor matrices for the quality assessment of the results in terms of precision and reliability can be determined. It should be noted critically that these calculations are almost always performed in linear or linearized models that are used instead of the original nonlinear problem, which can lead to bias effects.
In the Special Issue, the current developments in the field of stochastic modelling will be presented and illustrated by means of practical examples.
Prof. Dr. Frank Neitzel
Manuscript Submission Information
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- Measurement uncertainty
- Variance–covariance matrix
- Covariance functions
- Variance–covariance propagation
- Adjustment computations
- Bias effects
- Statistical tests
- Model testing and optimization
- Multi sensor systems
- Surface approximation