# 2D Network Simulation

Hello Zaklina,

I successfully simulated 2D network

Perfect!

Is there any chance to calculate RELATIVE ellipses parameters?

Yes, there is a - let's say - workaround. You can use a part of the implemented deformation analysis because the estimation procedure is identical. To obtain the confidence region between any two points, you must specify both points as a so-called point nexus, cf. the bottom screenshot in the right figure. The relative confidence region is shown in the plot in yellow (default color) together with an arrow. As already mention, the function is designed for deformation analysis, so the arrows indicate the direction of deformation. So, please ignore the arrows. In the Figure, relative confidence regions are depicted between S1 and S4 as well as S2 and S3.

Also, can you help me put instrument centering error in stochastic model? I put these values in stochastic model: 1" for δa for directions, and for distances δa 1mm, and δc(d) 1.5ppm (declared accuracy of instument). I need to put value 0.8mm for instrument centering error, can you help me?

The coefficients σa, σb and σc have no physical meaning. They differ only in the way the distance between the standpoint and the target point is taken into account. For this reason, you must consider how a centering error will affect a measurement. For a distance measurement, the centering error refers to the line of sight but is independent of the distance between the standpoint and the target point. Thus, σa should be used to specify the centering error by applying uncertainty propagation, i.e. $\sigma_a = \sqrt{1.0^2 + 0.8^2}$. For a angle measurement, the centering error is transverse to the line of sight. Moreover, the unit of the centering error is usually not an angle unit, like your specified 0.8 mm. To convert the transverse error component to an angle unit, the distance between the standpoint and the target point is needed, i.e. $\frac{σ}{d}$. Thus, the centering error is part of σc for angle measurements.

What means δb√d in stochastic model also?

Typically, leveling networks are specified for 1 km double measuring a line. Here, the σb must be used to specify the stochastic model.

Best regards

Micha

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