CL653 - State Estimation: Theory and Applications
Depends on previous exposure. But all preliminaries are covered from the basics, so it is manageable.
Time Commitment Required
Can vary highly from person to person. More or less like an average core course
Grading Policy and Statistics
Grading was done based on 4 assignments(15 %), midsem(15 %), endsem (45 %) and course projects (25 %).
Prior knowledge of probability theory and statespace representations would be advantageous and not necessary.
4 assignments(15 %), midsem(15 %), endsem (45 %) and course projects (25 %).
Topics Covered in the Course
The course starts with an introduction to the basics of probability theory, system modelling and weighted least squares, followed by various estimation algorithms (mainly Kalman filters).
Despite the course content being new, all the topics are taught from scratch, so its not that difficult to follow.
Actual implementation of the algorithms provides a much better understanding. Assignments and projects were both really well designed.
Feedback on Exams
Midsems and Endsems were comparatively easier than the content covered in class.
Motivation for taking this course
I am interested in the study of control systems, of which estimation is an integral part. Kalman filters in particular have been applied to a wide range of fields. This course provides an intuitive understanding, rigorous maths as well as implementation of Kalamn filters.
Better understanding of the algorithms thorugh implementation to real life physical systems
How strongly would I recommend this course?
I think this is something which is required in almost all engineering disciplines. The course structure is really good, so is the teaching. Overall I feel this is a pretty good course for those who are interested in core engineering.
When to take this course?
5th semester; 5th or 7th semester is ideal in my opinion
You can always chose to continue your course project with the professor even after the semester.
- A. Gelb (editor), “Applied Optimal Estimation”, M.I.T. Press, 1988.
- D. Simon, “Optimal State Estimation: Kalman, H∞ and Nonlinear Approaches”, Wiley Interscience.
- J. L. Crassidis, J. L. Junkins, “Optimal Estimation of Dynamic Systems”, CRC Press.
- T. Soderstorm, “Discrete-time Stochastic Systems: Estimation and Control”, Springer,
2nd Ed., 2002.
- P. S. Maybeck, “Stochastic Models, Estimation and Control”, Vol. 1, Academic Press,
- A. H. Jazwinski, “Stochastic Processes and Filtering Theory”, Academic Press, 1979.
- R. Branko, S. Arulampalam, N. Gordon, “Beyond the Kalman Filter: Particle Filters
for Tracking Applications”, Boston: Artech House, 2004
CL 653 Review By: Mitalee Oza