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Contents
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| Week 1: |
Probability concepts, distributions, density function, distribution function, measurements as random variables, moments, ecpexted value, variance, variance-covariance matrix |
| Week 2: |
Distribution of observations and functions of observations , accuracy criteria, weight concept, variance-covariance propagation, root mean square error, numerical applications |
| Week 3: |
Parameter estimation, Gauss-Markof model, adajustment computation, least squares method, |
| Week 4: |
Functional model; observation equations, linearization, numerical applications, homework |
| Week 5: |
Stochastic model; Normal equations and their solutions, calculation of unknowns, calculation of square mean error of parameters, numerical applications |
| Week 6: |
Writing observation equations of length and, direction measurements in horizontal control networks, homework |
| Week 7: |
midterm |
| Week 8: |
unconstrained networks, datum of a geodesic network, adjustment |
| Week 9: |
minimaly constrained adjustment, numerical applications
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| Week 10: |
outlier detections |
| Week 11: |
reports on adjustment results and error ellipses |
| Week 12: |
2D-3D Coordinate Transformation, numerical applications |
| Week 13: |
GNSS Network Adjustment |
| Week 14: |
GNSS Network Adjustment, numerical applications |
| Week 15*: |
- |
| Week 16*: |
Final Exam |
| Textbooks and materials: |
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| Recommended readings: |
Adjustment computations: spatial data analysis: by Charles D. Ghilani fifth edition, Hoboken, John Wiley & Sons, Inc., 2010, 647pp
Koch KR (1998) Parameter estimation and hypothesis testing in linear models. Springer, Berlin
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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