Lecture Description
By the end of the course, students will be able to:
- Distinguish between random, systematic, and gross errors (outliers).
- Understand the general law of error propagation and apply it to various practical cases.
- Understand the principle of the method of least squares and explain its widespread use in practice.
- Be familiar with the concepts of indirect and conditional least-squares adjustments, distinguish between them, and combine them as appropriate.
- Independently implement and solve both linear and nonlinear adjustment problems using MATLAB or a higher-level programming language.
- Statistically evaluate the results of least-squares adjustments in a correct and rigorous manner.
- Be familiar with various data filtering methods and implement them independently in MATLAB or a higher-level programming language.
- Place Fourier analysis in the context of least-squares adjustment and understand its role in data filtering.
- Understand the progression from sequential adjustment methods to the Kalman optimal filter.
