Academics / Courses / DescriptionsELEC_ENG 463: Adaptive Filtering and Estimation
This course is not currently offered.
Prerequisites
ELEC_ENG/COMP_ENG 395-0 (Probabilistic Systems) or ELEC_ENG 422-0 (Random Processes in Communication and Control I) recommended.Description
Applications of adaptive filtering to speech processing and noise cancellation, autoregressive-moving-average (ARMA) models, linear prediction, stochastic gradient least mean squares algorithm, least squares estimation, Kalman filter.
COURSE DIRECTOR: Prof. Michael Honig
REQUIRED TEXT: Simon O. Haykin. (2013). Adaptive Filter Theory, 5th Edition. Pearson. ISBN-13: 978-0132671453
COURSE GOALS: To provide first-year graduate students with an understanding of adaptive filtering applications, structures, algorithms, and performance.
COURSE TOPICS:
- Applications of adaptive filters
- Autoregressive and Moving Average processes
- Linear prediction and joint process estimation
- Lattice filters
- Gradient and stochastic gradient (Least Mean Square) algorithms
- Least squares filtering
- Kalman filter
- Convergence analysis
GRADES: A weighted combination of homework, midterm, and final.
COURSE OBJECTIVES: When a student completes this course, s/he should
be able to:
- Compute optimal linear prediction filters from second-order
input statistics.
- Design an LMS algorithm to meet convergence and steady-state
performance constraints.
- Design an adaptive lattice filter, both for prediction and
joint-process estimation.
- Design recursive Least Squares and Kalman filters
for different applications.
- Specify convergence and steady-state performance of the
preceding techniques by either analysis or simulation.