**Instructor**

Srikrishna Bhashyam

Office: ESB2 405

Phone: 2257 4439

**Pre-requisites**

Probability Foundations for Electrical Engineers (EE5110 or EE3110).

**Course Description**

This is a graduate-level course on adaptive filters. The design and performance of adaptive filters are discussed. Two classes of algorithms -- stochastic gradient algorithms and least squares algorithms -- to adapt the coefficients of a linear filter are discussed in detail. The topics covered are:

1) Review of Estimation Theory

--- Minimum Mean Squared Error (MMSE) estimation

--- Linear MMSE estimation

--- Sequential linear MMSE estimation

--- Kalman filter

2) Stochastic Gradient Algorithms

--- Least Mean Squares (LMS) Algorithm

--- Mean-square performance

--- Transient performance

3) Least Squares Algorithms

--- Recursive Least Squares (RLS) algorithm

--- Kalman filtering and RLS algorithm

4) Other topics from:

--- Array Algorithms

--- Lattice Filters

--- Robust Filters

--- Other performance criterion (other than MMSE and LS)

**References**

[1] A. H. Sayed, Adaptive Filters, John Wiley & Sons, NJ, ISBN 978-0-470-25388-5, 2008. Video Lectures here

[2] S. Haykin, Adaptive Filter Theory, Fourth Edition, Pearson Education LPE, 2007.

[3] Alexander D. Poularikas, Zayed M. Ramadan, Adaptive filtering primer with MATLAB, CRC Press, 2006.

[4] B. Widrow and S.D. Stearns, Adaptive Signal Processing, Prentice Hall, Englewood Cliffs, NJ, 1985.

**Lecture Notes**

2018 Lecture Notes

**Evaluation**

As per Institute Academic Calendar

Quiz 1 (20%) -- Aug 30, 2023

Assignment (15%)

Project (20%)

Final (45%) -- Nov 20, 2023