| Course Name |
(中) 高等測量平差法(二)(6748) |
| (Eng.) Advanced Data Adjustment(II) |
| Offering Dept |
Department of Civil Engineering |
| Course Type |
Elective |
Credits |
3 |
Teacher |
YEN-RU LAI |
| Department |
Department of Civil Engineering/Graduate |
Language |
English |
Semester |
2026-FALL |
| Course Description |
This course extends Advanced Data Adjustment (I) and covers covariance analysis, variance component estimation, time-series analysis, robust estimation, sequential adjustment, and Kalman filtering. It further introduces inverse problems, regularization, nonlinear inversion, and Bayesian inversion for multi-source and multi-temporal geodetic and geophysical data. |
| Prerequisites |
|
self-directed learning in the course |
Y |
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| This course aims to develop students’ understanding of advanced data adjustment, covariance analysis, and precision assessment, while introducing time-series analysis, sequential adjustment, and dynamic estimation methods. Students will also learn the fundamentals of inverse problems, regularization, nonlinear and Bayesian inversion, and develop practical skills for analyzing and integrating multi-source and multi-temporal geodetic and geophysical data. |
|
|
| Lecturing |
| topic Discussion/Production |
| Discussion |
|
| Written Presentation |
| Oral Presentation |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Review of Least-Squares Adjustment |
| Week 2 |
Covariance Propagation and Precision Assessment |
| Week 3 |
Stochastic Models and Weight Matrix Design |
| Week 4 |
Variance and Covariance Component Estimation |
| Week 5 |
Time-Series Analysis and Performance Metrics |
| Week 6 |
Outlier Detection and Robust Estimation |
| Week 7 |
Sequential Least-Squares Adjustment and Kalman Filtering |
| Week 8 |
Midterm (Project Proposal and Progress Presentation) |
| Week 9 |
Fundamentals of Geophysical Inverse Theory |
| Week 10 |
Generalized Inverse, Singular Value Decomposition, and Ill-Posed Problems |
| Week 11 |
Regularization Methods for Inverse Problems |
| Week 12 |
Nonlinear Estimation and Inversion |
| Week 13 |
Bayesian Estimation and Uncertainty Quantification |
| Week 14 |
Joint Inversion and Multi-Source Data Integration |
| Week 15 |
Time-Dependent Inversion and Geodetic/Geophysical Applications |
| Week 16 |
Final Project Presentation |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
| Midterm (40%), Final Project (60%) |
| Textbook & other References |
Textbook:
Ghilani, C. D. (2018). Adjustment Computations: Spatial Data Analysis, 6th ed., Wiley.
References:
Aster, R. C., Borchers, B., & Thurber, C. H. (2018). Parameter Estimation and Inverse Problems, 3rd ed., Elsevier.
Menke, W. (2018). Geophysical Data Analysis: Discrete Inverse Theory, 4th ed., Academic Press.
Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples, 4th ed., Springer. |
| Teaching Aids & Teacher's Website |
| iLearning |
| Office Hours |
| By appointment via email. |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|