University of California, Santa Barbara
Department of Electrical and Computer Engineering

Kalman and Adaptive Filtering

ECE 248 - Fall 2010

Instructor: Prof. Katie Byl
(katiebyl@ece.ucsb.edu)

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Homework

Lecture Materials

Other Information

Course Description: An introductory course in Kalman filtering and related estimation techniques.

Topics and Lecture Schedule: See Other Information link.

Prerequisites: Linear systems theory (e.g., ECE 230A) and some background in probability / random processes (e.g., ECE 140), both of which will be reviewed in the first weeks of class.

Office Hours: Monday 2-4pm (and/or by appointment), in HFH 5115.

Suggested Texts / Other Readings:
The primary text (recommended, but not required) for the course is:
Introduction to Random Signals and Applied Kalman Filtering (3rd edition), by Robert Grover Brown and Patrick Y.C. Hwang. (1997.) ISBN-10: 0-471-12839-2
- See also the numbered list at the Other Information link.

Grading: (still tentative) Homework (40%), Mid-Term Exam (25%), and Final Project (35%).

Homework: Expect 4 or 5 HW assignments, total.

Mid-Term Exam: Covers topics through October 20. See Other Information link. (Date and format TBA.)

Final Project: MATLAB project simulation, on a sub-topic of choice. Deliverables include a short written report and m-file(s) your classmates can run that illustrate techniques or issues in filtering. Examples: A) Extended KF, B) Particle Filtering, C) Unscented KF, D) Inertial Navigation, E) GPS (Global Positioning System), F) Adaptive (Multiple Model) KF.

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Last Updated: September 27, 2010