Instructor: Prof. Kevin M. Passino 416 Dreese Laboratory, passino [at] ece.osu.edu

Office Hours: Talk to me after class or email me and set up an appointment

Textbook: D. Simon, Optimal State Estimation, Wiley, NY, 2006

Course Objectives: Provide a course for graduate and undergraduate students that treats the basics of estimation with applications to filtering and control. This will be a "hands-on" course where Matlab will be used extensively. Applications problems will be chosen from automotive systems, robotic systems, process control and others.

Topical Outline:

1. Introduction
2. Batch least squares
3. Recursive least squares
4. Properties of least squares estimators
5. Best linear unbiased estimator
6. Maximum likelihood estimation
7. Mean squared estimation
8. State Estimation, the Kalman filter
9. Kalman filtering examples
10. Extended Kalman filter
11. Kalman-Bucy filter

Grading (tentative):

• Homework, 15%
• Projects, 30%
• Midterm, 25%
• Final Exam, 30%

Policy: Work entirely on your own for all assignments. Turn in your code for programming assignments.

Prerequisites: EE 351, 352, Course on probability and random variables

Scheduling: This course is offered in Autumn Quarter

Links/Resources:

1. A. Gelb, ed., Applied Optimal Estimation, MIT Press, 1974.
2. J.M. Mendel, Lessons in Estimation Theory for Signal Processing, Communications, and Control, Prentice Hall, 1995.
3. J. Farrell and M. Barth, The Global Positioning System and Inertial Navigation, McGraw Hill, NY, 1999.
4. Mathworks (for links)
5. Wikipedia: http://en.wikipedia.org/wiki/Kalman_filter