Title : Adaptive Signal Processing

Course No : EE6110

Credits : 4

Prerequisite : Probability and Random Processes

Syllabus :

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)