ECE 603 - Spring 2017

ECE 603 - Statistical Signal Processing

Instructor

Professor Patrick Mitran

  • Office: EIT 4146
  • Office Hours: Wednesdays 3-4pm in EIT-4146.
  • Voice: (519) 888-4567 x37475
  • Email

Lecture Schedule

Tuesdays from 11:30-02:20pm in EIT-3141.

Note: It is possible that a regularly scheduled lecture may be cancelled. To compensate, please note that there is a makeup lecture scheduled for 23 May from 11:30-2:20pm. 23 May is officially a Monday schedule, and this makeup lecture is thus scheduled on a Monday scheduled day, from 11:30-2:20pm in EIT-3141.

Pre-requisite

Undergraduate probability as well as signals and systems. It is recommended to have taken a course in DSP, but this is not necessary.

Aims

This is a course in statistical signal processing. In particular, as opposed to a first course is digital signal processing which deals with deterministic signals, this course aims to present methods by which to design signal processing techniques in the presence of uncertainty. This will usually involve some form of the least squares methods in the context of signal processing and second order statistics will play an important role in some components. The emphasis will be on teaching how least squares methods yield signal processing algorithms as opposed to presenting cook-book solutions.

Course Outline

  1. Review of random processes: filtering; spectral factorization; special types of random processes.
  2. Signal modeling: least squares; Pade approximation; Prony's method; stochastic models.
  3. Linear prediction and optimum linear filters: Levinson recursion; lattice filters; Wiener filtering, Kalman filtering, Cramer-Rao bounds.
  4. Spectrum estimation: non-parametric methods; minimum variance; maximum entropy; parameric methods; frequency estimation.
  5. Adaptive filtering: LMS algorithm; recursive least squares; decision feedback equalizers.

Textbook and references

  • Textbook: Statistical Digital Signal Processing and Modeling by Monsoon H. Hayes
  • Suggested reference 1: Adaptive Filter Theory by Simon Haykin
  • Suggested reference 2: Statistical Signal Processing by Robert M. Gray. (available online)
  • Suggested reference 3: Toeplitz and Circulant Matrices: A Review by Robert M. Gray. (available online)

Course Evaluation

  • Problem sets will be handed out. You should attempt them all. Solutions will be posted on the course website.
  • There will be one simulation project asking you to implement some algorithms that will count for 15% of the grade.
  • There will be 1 in class midterm exam tentatively set for 17 June that will count for 35% of the grade.
  • There will be a final exam that will count for 50% of the grade.

I reserve the right to provide an alternative grading scheme as necessary if the class grades are below what I deem reasonable. I guarantee that no student shall receive a grade less than that of the official scheme above, but the alternate scheme, if any, may improve your grade.

Required inclusions

  • Academic integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility.
  • Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4. When in doubt please be certain to contact the department’s administrative assistant who will provide further assistance.
  • Discipline: A student is expected to know what constitutes academic integrity to avoid committing an academic offence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties check Guidelines for the Assessment of Penalties.
  • Appeals: A decision made or penalty imposed under Policy 70 (Student Petitions and Grievances) (other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals).
  • Note for students with disabilities: The AccessAbility Services, located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the AccessAbility Services at the beginning of each academic term.