COURSE TITLE: ECE 510-20 Seminar - Adaptive Filters (Winter 2004)

CATALOG DESCRIPTION: Applications of adaptive filtering, linear prediction, lattice filters, LMS algorithm, least squares filtering, reduced-rank filters, convergence analysis

REQUIRED TEXT: S. Haykin, Adaptive Filter Theory, Prentice-Hall, 2002.

COURSE DIRECTOR: M. Honig

COURSE GOALS: To provide first-year graduate students with an understanding of design and performance analysis techniques for adaptive filters in telecommunications applications.

PREREQUISITES BY COURSES: ECE 359 and ECE 422

PREREQUISITES BY TOPIC:

ITEM 1: Probability and random processes

ITEM 2: Frequency-domain (spectral) analysis

ITEM 3: Familiarity with z-transforms.

COURSE TOPICS:

1. Applications of adaptive filters

2. Linear prediction

3. Lattice filters

4. The Least Mean Square (LMS) algorithm

5. Least squares filtering

6. Reduced-rank filters

7. Convergence analysis

COMPUTER PROJECTS: Optional.

LABORATORY PROJECTS: None.

GRADES: TBD

COURSE OBJECTIVES: When a student completes this course, s/he should be able to:

1. Compute optimal linear prediction filters from second-order input statistics.

2. Design an LMS algorithm to meet convergence and steady-state performance constraints.

3. Design an adaptive lattice filter, both for prediction and joint-process estimation.

4. Design a computationally efficient Least Squares filter for different applications.

5. Specify convergence and steady-state performance of the preceding techniques by either analysis or simulation.