Of course there is a second way to judge the precision of the 
adjustment result, that is by simply looking to the outcoming 
standard-deviations of the x- and y-coordinates. This method, however, 
has one disadvantage namely the dependency of the chosen S-base. This 
means that an other S-base results in other standard deviations, so 
one has to be very careful. 
However, to be honest, it should be stated that applying the general 
eigenvalue problem has also a disadvantage that is in terms of storage 
capacity of computers. For each of the, relatively small, blocks about 
5 Mb is needed. This results in the fact that in practice the precision 
evaluation would not be carried out with the mentioned eigenvalue 
problem, but by judging the outcoming standard deviations. 
But still for research purposes the eigenvalue problem is a very handy 
tool, because not only the precision of the whole network can be looked 
upon, but also parts of it. In this way weak parts of the network can 
be traced. However, there is one condition that has to be fulfilled. 
When dealing with partial networks or blocks both the G- and the H- 
matrix has to undergo an S-transformation to a base (and again the 
same base) within the partial block, but this can be done quite easily. 
5. THE RESULTS OF THE EXPERIMENTS 
As mentioned the experiments were carried out with three different 
block configurations. The total number of experiments was six, that 
is two experiments with block 1, one with block 2 and three with 
block 3. 
For all experiments the following results are presented. 
|. precision of x- and y-coordinates after the free block adjustment 
(first phase) in terms of eigen values om 
2. precision of the x- and y-coordinates after the pseudo least squares 
adjustment in terms of eigen values 
3. reliability of ground control points, calculated in the second phase 
of the adjustment, in terms of boundary values. 
The precision results are shown in two tables. Table 1 shows the 
maximum eigenvalues resulting from all experiments. | 
In tables 2 to 5 the boundary values of the control points are listed 
for each experiment. 
Before evaluating the results of the six experiments first some 
additional information about each experiment will be given. 
experiment | 
block used: see figure 2. 
distribution of tie-points: 4 per model (see chapter 3.1). 
number of tie-points: 120 
number of control points: 30 
distribution of control points: see figure 2 
chosen partial networks with respect to the eigenvalue 
calculations: 
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