ECE 7630 - Advanced Digital Signal Processing



Syllabus - Fall 2007

Course Title:

Advanced Digital Signal Processing

Instructor:

Dr. Scott E. Budge

Office:

EL 113

Phone:

797-3433 (Office), 753-5931 (Home)

Office Hours:

T-R 3:00-5:00 pm
Other hours by appointment

Lecture Time:

T-R 1:30-2:45

Lecture Place:

ENGR 304

Prerequisite Topics:

You should have an understanding of the topics covered in ECE 5630 and ECE 6010. Prerequisites include:

1. Working knowledge of linear system theory, including convolution, Z and Fourier transforms, sampling, and the DFT.
2. Working knowledge of linear algebra.
3. Familiarity with stochastic processes and probability theory, including correlation and power spectral density functions.

Textbook:

Proakis, J. G., et al, Algorithms for Statistical Signal Processing, Prentice Hall, New Jersey, 2002.

Reference:

(The following may be helpful.)

1. Manolakis, D., Ingle, M., Kogon, S., Statistical and Adaptive Signal Processing, McGraw-Hill, 2000.
2. Haykin, S.,Adaptive Filter Theory, third edition, Prentice Hall, New Jersey, 1996.
3. Proakis, J. G., Manolakis, D. G., Digital Signal Processing Principles, Algorithms, and Applications, third edition, Macmillan, New York, 1996.
4. DeFatta, D. J., Lucas, J. G., Hodgkiss, W. S., Digital Signal Processing: A System Design Approach, John Wiley and Sons, New York, 1988.
5. Oppenheim, A. V., Schafer, R. W., Digital Signal Processing, Prentice-Hall, New Jersey, 1975.
6. Oppenheim, A. V., Schafer, R. W., Discrete-Time Signal Processing, Prentice-Hall, New Jersey, 1989.
7. Scharf, L. L., Statistical Signal Processing, Addison-Wesley, 1991.
8. Porat, B., Digital Processing of Random Signals, Prentice Hall, 1994.

Course Summary:

This course is a continuation of the principles taught in ECE 5630. It will include topics on advanced digital signal processing (DSP) which are used in many different fields of application. Topics include linear prediction and optimal filter design, including Weiner and Least-Squares filters. Students will select topics of interest from adaptive filtering methods, spectral estimation, and array processing.

Course Objectives:

At the completion of the course the student will have a knowledge of the theory and applications of DSP in the area of optimal filtering. Other topics of interest to the class will also be covered. The student will be introduced to the theory with enough detail to be able to read and understand the literature and perform original research.

Possible outcomes - At the end of the course, students will be able to do selected items in the following list, based on content covered:

1. Demonstrate understanding of AR, MA, and ARMA models and how the parameters are estimated.
2. Demonstrate understanding of linear Minimum-Mean-Square-Error (MMSE) optimal filter design.
3. Demonstrate understanding of solutions to the normal equations, including Levinson-Durbin, Schür, and LDL.
4. Demonstrate understanding of optimal lattice and lattice-ladder structures for prediction and filtering.
5. Demonstrate understanding of optimal linear Least-Squares (LS) filter design.
6. Demonstrate understanding of LS computational techniques (orthogonalization).
7. Demonstrate basic understanding of Least-Mean-Square (LMS) and Recursive-Least-Square (RLS) adaptive filtering.
8. Demonstrate basic understanding of parametric and nonparametric methods for spectral estimation.

Course Accessibility:

In cooperation with the Disability Resource Center, reasonable accommodation will be provided for qualified students with disabilities. Please meet with the instructor during the first week of class to make arrangements. Alternate format print materials (large print, audio, diskette or Braille) will be available through the Disability Resource Center.