| dc.description.abstract | A fundamental consideration in densification of geodetic
networks is how to handle the position values of the already
established datum stations. The question is: shall they be
considered as stochastic or as fixed, non-stochastic entities?
Different densification models have been put forward as
solutions to the question above. These are distinguished by the
manner in which higher order net points are handled within the
densification process.
Presented herein is a study aimed at evaluating three
densification approaches, namely; static, dynamic, and static dynamic
densification models with a view to identifying their
strengths and weaknesses as models for densification of geodetic
networks. In the static densification model, existing stations are
held fixed and assumed errorless, while in the dynamic
densification model, the existing datum parameters are treated as
stochastic. The static-dynamic model treats datum parameters as
stochastic prior information, while at the same time keeping them
numerically and stochastically unchanged.
To evaluate these models, each was used to adjust a network at
two levels of densification. The adjustment process involved'
estimation of parameters for secondary and tertiary densification
networks built on a datum defined by adjusting the primary network
within the framework of a free network. For each model and at every
level of densification, the resulting parameters, standard errors
of points and their correspondi~g standard error ellipses were
'compared against each other. Through analysis of these results the
strength and weaknesses of each densification model have been
appraised.
A real network forming a part of the geodetic network of Kenya
was adopted as the test network. The network consists of eight
primary control stations, fifteen secondary stations, and twentytwo
tertiary stations. Using original field data the test network
is densified in two levels using the three densification models
above.
The results indicate that standard errors and point error
ellipses from the static model are the smallest, followed by those
from the static-dynamic model, and finally those from the dynamic
model. The standard errors for the static model are expected to be
small algebraically because they are based on a fixed and errorless
datum; with the datum being stochastic these results are not
representative enough.
The dynamic and static-dynamic densification models
incorporate stochasticity of datum parameters, in the staticdynamic
model datum parameters are maintained definitive, while in
the dynamic model all parameters are estimated afresh, thus
resulting in the loss of the concept of datum. It is on the basis
of the stronger theoretical and practical qualities of the static dynamic
model that the model would ordinarily be recommended for
geodetic densification of networks.
The results in general demonstrate that the static-dynamic
model gives more realistic estimates than the static and dynamic
models hence it is a more suitable approach to the densification of
geodetic networks. | en |
