COURSE TITLE: ECE 463: Adaptive Filters
CATALOG DESCRIPTION: applications of adaptive filtering,
autoregressive and moving average processes, linear prediction,
lattice filters, Least Mean Square (LMS) algorithm,
least squares filtering, 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 adaptive filtering applications, structures, algorithms,
and performance.
PREREQUISITES BY COURSES: 359, 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. Autoregressive and Moving Average processes
3. Linear prediction and joint process estimation
4. Lattice filters
5. Gradient and stochastic gradient (Least Mean Square) algorithms
6. Minimum output energy methods
7. Least squares filtering
8. Convergence analysis
9. Reduced-rank filtering
COMPUTER PROJECTS: Optional.
LABORATORY PROJECTS: None.
GRADES: A weighted combination of homework, midterm, and final.
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.