650 Introduction to Estimation
Office hours: By
appointment (email: firstname.lastname@example.org)
is preferred, but feel free to knock at my door anytime.
knowledge of probability and random variables, discrete-time systems,
z-transforms, signals and systems basics: EE 352, and Math 530 or Stat
Please note: The official web
page of the course is developed on WebCT at class.osu.edu
Registered students may access all the additional information on the course, including notes, homework sets, solutions, and the update syllabus through the WebCT system.
Textbook and useful references:
The course introduces the students to parameter estimation and state
estimation in linear systems. Topics include: linear dynamic systems
with random inputs, least squares estimation, mean-squared estimation,
maximum likelihood,and Kalman filtering with applications in electrical
|1. Basic probability theory.
Linear estimation models
|2. Batch least squares.|
|3. Recursive least squares.|
|4. Properties of estimators.|
|5. Properties of LS estimators.|
|6. Best linear unbiased estimators.|
|7. Maximum likelihood estimators.|
|8. Gaussian Random Variables (review).|
|9. Minimum variance estimators. Conditional expectation.|
|10. Properties of minimum variance estimators.|
|11. State estimation: Kalman predictor.|
|12. State estimation: Kalman filter.|
|13. Extended Kalman filter.
|14. Kalman-Bucy filter.
Return to Andrea Serrani's home page