In this lab you will apply what you have been learning in class (about the linearization of functional models, and about stochastic models for geomatics networks) in order to carry out the preanalysis of a trilateration network.

This lab has two parts:

1. In Part 1, you’re asked to add to and use your C++ library from Lab 2 so that it can carry out a network preanalysis in an iterative fasion.

2. In Part 2, you’re asked to carry out such a preanalysis using the real world network scenario provided.

These two parts are described in the lessons below.

Why this lab?

Specifically, you have four goals in doing this lab:

1. To demonstrate an understanding of stochastic modeling and error propagation as it applies to the design of horizontal control networks

2. To develop C++ code and an application that applies / implements your understanding in a practical context

3. To demonstrate that you can use this in your own practice as a geomatics engineer to design a network, e.g. to help avoid excessive or inadequate field observations

4. To reflect on the advantages and disadvantages of doing preanalysis before executing a survey project

And, simply put, these are fundamental skills for a practicing geomatics engineer.

Attribution

This lab is based on a similar lab delivered by Dr. Edward Krakiwsky and Dr. Mohamed Abousalem back in 1995 to students of the Geomatics Engineering program at the University of Calgary. I am grateful to them for their willingness to let me modify and share it here. Any mistakes are very likely mine and anything you like is very likely theirs.

Deadlines

The due dates for this work are outlined on our course page.

Assessment

Detailed rubrics will be handed out and discussed in class.