ELEC_ENG 463: Adaptive Filtering and Estimation

This course is not currently offered.


ELEC_ENG/COMP_ENG 395-0 (Probabilistic Systems) or ELEC_ENG 422-0 (Random Processes in Communication and Control I) recommended.


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.


  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. Least squares filtering
  7. Kalman filter
  8. 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:

  1. Compute optimal linear prediction filters from second-order

input statistics.

  1. Design an LMS algorithm to meet convergence and steady-state

performance constraints.

  1. Design an adaptive lattice filter, both for prediction and

joint-process estimation.

  1. Design recursive Least Squares and Kalman filters

for different applications.

  1. Specify convergence and steady-state performance of the

preceding techniques by either analysis or simulation.