ECE 771 Algorithms for Parameter and State Estimation
Term I 2017-2018



You can download ECE 705 and ECE 707.

Instructor:
T. Kirubarajan (Kiruba)
Electrical & Computer Engineering Department
Phone: x 24305
Email: kiruba at mcmaster dot ca
Web: http://www.ece.mcmaster.ca/~kiruba
Office: ITB-A112
Office hours: Tue./Fri. 9:30-11:30 and Thu. 12:30-14:30 (Open-door policy outside office hours)
Course page: http://www.ece.mcmaster.ca/~kiruba/ece771/

Class Schedule:
Thursdays 9:45am-12:45pm, ITB-113B

Summary:
This course presents parameter and state estimation algorithms for noisy dynamic systems. The objective is to present a comprehensive coverage of advanced estimation techniques with applications to communications, signal processing and control. In addition to theory, the course also covers practical issues like filter initialization, software implementation, and filter model mismatch. Advanced topics on nonlinear estimation and adaptive estimation will be discussed as well. The concepts will be put into practice by the students on realistic estimation projects.

Prerequisites:
Engineering mathematics, linear systems, probability and stochastic processes

Course Outline:
1. Basic concepts: Maximum likelihood (ML) estimation, Maximum a posteriori (MAP) estimation, Least squares (LS) estimation, Minimum mean square error (MMSE) estimation, Linear MMSE (LMMSE) estimation
2. LS estimation for linear and nonlinear systems
3.
Modeling stochastic dynamic systems
4. The Kalman filter for discrete time linear dynamic systems with Gaussian noise

5. Steady state filters for noisy dynamic systems

6. Adaptive multiple model estimation techniques

7. Nonlinear estimation techniques

8. Computational aspects of discrete time estimation

9. Extensions to autocorrelated noise and smoothing

10. Continuous time state estimation

Text:
Y. Bar-Shalom, X. Rong Li and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley & Sons, 2001.

Additional References:
1. F. L. Lewis, Optimal Estimation, John Wiley & Sons, 1986.

2
. R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, John Wiley & Sons, 1992.

Grading:
Exams 40%; Projects 30%; Homework assignments 30%

Policy Reminders:
The Faculty of Engineering is concerned with ensuring an environment that is free of all adverse discrimination. If there is a problem, that cannot be resolved by discussion among the persons concerned, individuals are reminded that they should contact the Department Chair, the Sexual Harassment Officer or the Human Rights Consultant, as soon as possible.

Students are reminded that they should read and comply with the Statement on Academic Ethics and the Senate Resolutions on Academic Dishonesty as found in the Senate Policy Statements distributed at registration and available in the Senate Office.

Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: "Grade of F assigned for academic dishonesty"), and/or suspension or expulsion from the university. It is your responsibility to understand what constitutes academic dishonesty. For information on the various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3.

The instructor and university reserve the right to modify elements of the course during the term. The university may change the dates and deadlines for any or all courses in extreme circumstances. If either type of modification becomes necessary, reasonable notice and communication with the students will be given with explanation and the opportunity to comment on changes. It is the responsibility of the student to check their McMaster email and course websites weekly during the term and to note any changes.

Last updated: September 7, 2017.