Last updated by
Huaan Fan on 2010-09-03.
- generalized matrix inverses and free network adjustment
- estimation of variance-covariance components
- gross error detection using the method of data snooping
Participants of this
course are assumed to have basic knowledge in theory of errors,
especially on error propagation, least squares principle and adjutment by elements.
For
students in KTH's master programme " A complete description of the course's aim, contents, requirements, etc can be found at KTH:s online Study handbook. Except 5 lectures, the
course will be run mostly as a project work to be carried out during a period of about 4 weeks.
Time schedule of the lectures is decided after consultation with students.
Other suitable but Norges Geogr. Oppm. Publ. 3/1984. Rao, C.R. (1973). Linear Statistical Inference and Its
Applications.
John Wiley & Sons.Rao, C.R. and S.K. Mitra (1971). Generalized Inverses of Matrices
and Its Apllications. John Wiley & Sons.
The project work concerns analysis of a simulated 2D geodetic network. Each student will first make a least squares adjustment of the network in the traditional way and then choose one of the following three analysis tasks: - Treat the network as a free network without any fixed points. Calculate
the least squares minimum-norm solution for the unknown coordinates. In
particular, students are requested to numerically investigate: (a) the coordinates of the centre of the network before and after
the free network adjustment; (b) the rotation of each radial vector as
well as the total rotation of the network due to free network adjustment and
(c) the scale change of each radial vector as well as the total scale change
for the whole network after the adjustment.
- Estimate the variance components for angles and distances, respectively,
using the Best Quadratic Unbiased Estimator (BQUE).
- Use data snooping to search
for gross errors for three different situations: (a) only
*one big*gross error, (b)*several "moderate"*gross errors and (c)*several**big*gross errors.
The project work should be documented in a
project report, which should clearly describe the input data sets, methods and
formulas used, numerical results and analysis of the results. In case two persons work together and submit the same report, then they should submit also a short declaration which states clearly who has done what in the project, and who has written which chapters/sections.
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