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ECE Course Syllabus

ECE6271 Course Syllabus


Adaptive Filtering (3-0-3)

Technical Interest
Digital Signal Processing

ECE 4270


Catalog Description
Basic theory of adaptive filter design and implementation. Steepest descent, LMS algorithm, nonlinear adaptive filters, and neural networks. Analysis of performance and applications.

Sayed, Ali H., Fundamentals of Adaptive Filtering, Wiley and Sons, 2003. ISBN 9780471461265(optional)

Indicators (SPIs)
SPIs are a subset of the abilities a student will be able to demonstrate upon successfully completing the course.

Topical Outline
Background (1 week)
      Review of Discrete-time random processes 
FIR Wiener filters (1 week)
      Derivation of the Wiener-Hopf equations
      Principle of orthogonality
      Problems and applications
      Solving the Wiener-Hopf equations.
The Discrete Kalman Filter (1 week)
Gradient-based adaptive filters (4 weeks)
      Steepest descent
      The LMS algorithm
      Performance Analysis
      Variations on the LMS algorithm 
      Examples and comparison of techniques
Gradient Adaptive Lattice Filter (0.5 weeks)
Recursive least squares (1.5 weeks)
      Transversal filters 
      Lattice filters - optional
      Performace of the RLS algorithm
Tracking of time-varying systems (0.5 weeks) - Chapter 16 of Haykin
Adaptive IIR filters (1.5 weeks)
      IIR LMS
      Fientuch and Horvath algorithms
      HARF and SHARF
      Examples and applications
Nonlinear adaptive filters (3 weeks) - Chapters 18-20 of Haykin
      Order statistic adaptive filters and Volterra systems
      Blind deconvolution - decision directed feedback 
      Back propagation learning
      Radial basis function networks
Other Applications - optional
     Adaptive line enhancement
     Adaptive spectrum estimation, frequency tracking
     Adaptive signal modeling