Simon Haykin, Adaptive Filter Theory, fourth edition, Prentice Hall, 2002. Optional
. Software: MATLAB Student Version 7.x, release 14. An interactive numerical ...
ECE 6650 Estimation Theory and Adaptive Filtering Fall Semester 2015 Instructor:
Dr. Mark Wickert Office: EB-226
[email protected] http://www.eas.uccs.edu/wickert/ece6650/
Phone: 262-3500 Fax: 262-3589
Office Hrs:
Thurs. 2:15–3:00 pm, others by appointment. Note: These hours may be adjusted if needed.
Req. Text:
Simon Haykin, Adaptive Filter Theory, fifth edition, Prentice Hall, 2013.
Optional Software:
Python (www.ipython.org) or MATLAB. Both provide a vectorized programing language for scientific and engineering problem solving. With Python, Scipy provides the signal package while in MATLAB the signal processing toolbox is available. Extra signal processing support in Python is provided by code custom modules such as ssd.py available at http://www.eas.uccs.edu/wickert/SSD/.
Grading:
1.) Graded homework assignments, both Problems and Computer-oriented Problems worth 60%. 2.) Midterm exam, most likely a take-home computer simulation problem, worth 20%. 3.) Final exam or project worth 20%.
Topics 0. Background and Preview 1. Stochastic Processes and Models 2. Introduction to Estimation Theory 3. Wiener Filters 4. Linear Prediction 5. Method of Steepest Descent 6. Method of Stochastic Gradient Descent 7. Least-Mean-Square (LMS) Algorithm 8. Normalized Least-Mean-Square Algorithm and Its Generalization 9. Block-Adaptive Filters 10. Method of Least Squares 11. Recursive Least-Squares Adaptive Filters 12. Robustness 13. Finite-Precision Effects 14. Adaptation in Nonstationary Environments 15. Kalman Filters
Text Sections 0.1–0.8 1.1–1.17 App. D & notes 2.1–2.9 3.1–3.11 4.1–4.6 4.1–5.4 6.1–6.12 7.1–6.5 8.1–8.6 9.1–9.15 10.1–10.9 11.1–11.7 12.1–12.4 13.1–13.11 14.1–14.9
