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

Kalman and Adaptive Filtering

Schedule and Other Information

ECE 248 Homepage Homework Lecture Materials Other Information

Course Topics (updated regularly).
     Date(s) Lect.      Topic Other Details
     9/27 1      "A one-day hands-on overview." Intuitive explanation of the recursive DT Kalman algorithm.
     9/29,10/4 2,3      Probability theory Stochastic processes and their (most convenient) mathematical descriptors.
     10/6 4      Linear system theory Given some (potentially stochastic) input, what is the output?
     10/11 5      Least-squares estimation Recursive, matrix formulations; Review of matrix algrebra and calculus.
     10/13 6      Wiener filters LTI (MMSE) filter design: extract signal from noise (via freq. domain).
     10/18,10/20 7,8      Discrete-time (DT) KF
     TBA ---      Mid-term exam covers Lectures 1-8, above. ---
     -      Prediction (and other extensions)
     -      Continuous-time (CT) KF
     -      Smoothing
     -      Variations (multi-model adaptive, robust, ...)
     -      Unscented (UKF)
     -      Particle filters
     -      Inertial navigation; Sensor fusion

Additional Materials:
Our primary (suggested) text 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
Below are other possible texts that you may find useful. Some of the "classics" are quite affordable (as reprints).
    - Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, by Dan Simon. (2006.) ISBN-10: 0-471-70858-5
    - Applied Optimal Estimation, by Arthur Gelb. (1974.) ISBN: 978-0-262-20027-1 (HC), 978-0-262-57048-0 (PB)
    - Optimal Filtering, by Brian D. O. Anderson and John B. Moore. (1979.) ISBN: 0-13-638122-7, ISBN-10: 0486439380 (2005 reprint)
    - Optimal Control and Estimation, by Robert F. Stengel. (1986, 1994.) ISBN: 0-486-68200-5
Feel free to stop by my office to look at any or all of these good books.

Links to handouts for the class:
1) Introductory chapter by Maybeck is simply wonderful: it gives a very intuitive overview of Kalman filtering. (See Greg Welch's website and info on the digitized Maybeck chapter, too.)
2) Kalman Quickie appeared in the June 2010 issue of IEEE Control Systems Magazine. It's certainly quicker than Maybeck (in length), but also less intuitive for beginners, in my opinion. Thanks to Roy Smith for bringing this article to my attention.

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Last Updated: October 10, 2010