EE5027: Adaptive Signal Processing – Fall 2018

This graduate-level course provides an overview of many classical topics in adaptive signal processing:

• Review of signals, systems, random processes, and linear algebra.

• Wiener filters

• Linear prediction

• Kalman filters

• Method of steepest descent

• Stochastic gradient-based algorithms

• Least-mean-square adaptive filters

• Least-squares methods

• Recursive least-squares adaptive filters

News

• (New) December 21, 2018: Homework #4 updated [Version 20181221].

• December 18, 2018: Homework #4 updated.

• December 13, 2018: The deadline for Homework #4 is extended to December 26, 2018.

• November 29, 2018: Homework #4 posted.

• November 23, 2018: Homework #3 updated [Version 20181123] [AdaptiveSP_Problem_4e.mat].

• November 17, 2018: Handout #1 updated [Version 20181117].

• November 15, 2018: Kalman Filters in a Nutshell [Version 20181115].

• November 15, 2018: Homework #3 updated.

• November 15, 2018: Handout #1 updated.

• November 7, 2018: Homework #3 assigned.

• November 7, 2018: Handout #2 - Course calendar [Version 20181107].

• November 6, 2018: The article entitled “Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation” by R. Faragher is added to references.

• October 3, 2018: Homework #2 assigned [Version 20181003].

• September 26, 2018: Homework #1 updated [Version 20180926].

• September 19, 2018: Homework #1 assigned.

• September 19, 2018: Handout #1 - Administrative details and homework policies.

• September 11, 2018: Handout #2 - Course calendar.

• September 11, 2018: Handout #1 - Administrative details and homework policies

• August 27, 2018: The book A Short History of Circuits and Systems is added to the reference books.

• August 10, 2018: This course website is up. Welcome!

Course Information

• Lecture Hours: Wednesday 2, 3, 4 (9:10am to 12:10pm)

• Lecture Room: Barry Lam Hall Room 103 (博理103)

• Course Website:

  • Course materials, homework, announcements: http://homepage.ntu.edu.tw/~chunlinliu/AdaptiveSP_Fall_2018.html

  • Homework submission, discussion forum: https://ceiba.ntu.edu.tw/1071EE5027

• Instructor: Chun-Lin Liu (劉俊麟)

  • E-mail: chunlinliu@ntu.edu.tw

  • Office Hours: 9:00am to 10:00am on Tuesdays, or by appointment

  • Office Room: MD-515 (明達館515室)

• Grading: Homework (60%), midterm exam (20%), final report (1%+19%)

• Prerequisite: Calculus, linear algebra, probability and statistics, signal and systems

Course Materials

• Handout #1: Administrative details and homework policies (Last update: November 17, 2018) [Version 20181117]

• Handout #2: Course calendar (Last update: December 13, 2018) [Version 20181213]

• Slides: Kalman Filters in a Nutshell (Last update: November 15, 2018) [Version 20181115]

Homework Assignments

• Homework Assignment #1 [Version 20180926]

• Homework Assignment #2 [Version 20181003]

• Homework Assignment #3 [Version 20181123] [AdaptiveSP_Problem_4e.mat]

• Homework Assignment #4 [Version 20181221]

References

• F. Maloberti and A. C. Davies, Eds., A Short History of Circuits and Systems, River Publishers, 2016.
  Available at http://ieee-cas.org/short-history-circuits-and-systems.
  The history of adaptive filters and adaptive signal processing was reviewed in pp. 85-96.

• B. Widrow and S. D. Sterns, Adaptive Signal Processing, Prentice-Hall, 1985.

• S. Haykin, Adaptive Filter Theory, Fourth Edition, Prentice Hall, 2001.

• A. H. Sayed, Adaptive Filters, John Wiley & Sons, 2008.
  DOI: 10.1002/9780470374122

• T. Kailath, A. H. Sayed, B. Hassibi, Linear Estimation, Pearson, 2000.

• P. P. Vaidyanathan, The Theory of Linear Prediction, Synthesis Lectures on Signal Processing, Morgan and Claypool Publishers, 2008.
  Available at CaltechAUTHORS

• R. Faragher, “Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation [Lecture Notes],” in IEEE Signal Processing Magazine, vol. 29, no. 5, pp. 128-132, Sept. 2012.
  DOI: 10.1109/MSP.2012.2203621