Dear Mitcha,

I have a question regarding the adjustment of a levelling network. Here is the case, I have some fixed heights that are known from another adjustment as well as their corresponding variances. Now I have to include these heights in my adjustment and I don't want them to change, but I want to include their variances as well and not to assume they are error-free.
If I understand correctly the types of points in JAG3d, reference points are fixed points without uncertainties while stochastic points have uncertainties but they get new coordinates during the adjustment.
So, I would like to know is there a way to import fixed points with known variances in JAG3d and adjust the network in a way that these variances affect the results of the adjustment but the imported fixed points remain unchanged?

Thank you in advance!

Best regards,
Blaženka

Hello Blaženka,

So, I would like to know is there a way to import fixed points with known variances in JAG3d and adjust the network in a way that these variances affect the results of the adjustment but the imported fixed points remain unchanged?

No, there is no way because this contradict each other. You assume that the points have uncertainties. That means, the positions are only known to a certain extent, i.e., the points are improvable and, thus, not perfectly known (is not fixed).

In some federal states in Germany, a dynamic network adjustment (a network with stochastic points, as you mention) is performed as a final adjustment step to check the reference points for changes (e.g. outliers). If the point changes are insignificant, which means that the deviations are - let say - of random nature, the new adjusted positions are not transferred to the database or to the map (i.e. the new position is discarded).

Kind regards
Micha

This is not a contradiction, because a priori is different data than a posteriori. The point can remain fixed with additional stochastic information; e.g. think about the stochastic model of a mark or of a bolt. In theory, the point is fixed, but in practice, positioning at that point provides uncertainties.
The original a priori data could be kept - i.e. not overwritten -, and, in addition, a posteriori data could be provided as a result. Even for a fixed point, the a posteriori precision might be different.

I didn't try it, but maybe one could artificially introduce "observations of direction 0 gon, vertical zenith angle 0 gon, and distance 0m" and include the stochastic information into that artificial observation as kind of a workaround? For an error ellipsoid, one could introduce artificial observation in the directions of the principal axes.

So, I would like to know is there a way to import fixed points with known variances in JAG3d and adjust the network in a way that these variances affect the results of the adjustment but the imported fixed points remain unchanged?

No, there is no way because this contradict each other. You assume that the points have uncertainties. That means, the positions are only known to a certain extent, i.e., the points are improvable and, thus, not perfectly known (is not fixed).

Hello,

This is not a contradiction, because a priori is different data than a posteriori.

Yes, and that's why the position/parameter is improved by further observations. If both are identical, the parameter is fixed.

The point can remain fixed with additional stochastic information; e.g. think about the stochastic model of a mark or of a bolt.

Marking a point is a simple realization of a random experiment but with a single draw. The marked position is not the true value, i.e., if you repeat the marking procedure several times you will not end up with identical positions. These deviations are characterized by the stochastic model and the position is improved by further information e.g. observations in least-squares. It is simple application of statistic. You shouldn't draw conclusions from a single draw - that's a fallacy.

In theory, the point is fixed

Theoretically, there is a true position of the point that has certain coordinates. The true value is inherently not associated with any uncertainty. However, as long as you do not know the true position, you have to deal with uncertainties and imperfections.

I didn't try it, but maybe one could artificially introduce "observations of direction 0 gon, vertical zenith angle 0 gon, and distance 0m"

It is easy to verify that this procedure fails. Just take a look at the partial derivations.

Kind regards
Micha