**The Ohio State University**

**Dept. Electrical and Computer Engineering
ECE 650: Introduction to Estimation
Autumn 2008**

**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 O****bjectives: **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:**

- Introduction
- Batch least squares
- Recursive least squares
- Properties of least squares estimators
- Best linear unbiased estimator
- Maximum likelihood estimation
- Mean squared estimation
- State Estimation, the Kalman filter
- Kalman filtering examples
- Extended Kalman filter
- 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:**

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