Course Catalog 2007-2008

SGN-2606 STATISTICAL SIGNAL PROCESSING, 5 cr
Statistical Signal Processing

Courses persons responsible
Heikki Huttunen

Lecturers
Heikki Huttunen

Objectives
After passing this course the student will understand what estimation is, and how the methods can be applied in signal processing.

Content
Content Core content Complementary knowledge Specialist knowledge
1. Basic concepts of estimation. What is an estimator and how to compare their performance. Cramer-Rao lower bound for estimator variance.       
2. Classical tools for estimation of a deterministic parameter: minimum variance unbiased estimator (MVUE), sufficient statistic, best linear unbiased estimator (BLUE), least squares estimator.       
3. Estimation of nondeterministic parameters: the Bayesian approach.       
4. Practical signal processing applications will also be considered in each section.       

Requirements for completing the course
Final examination, weekly exercises and an assignment.

Evaluation criteria for the course

  • The final grade is 30% from the homeworks and 70% from the final exam.

  • Used assessment scale is numeric (1-5)

    • Study material
    Type Name Auhor ISBN URL Edition, availability... Exam material Language
    Book "Fundamentals of Statistical Signal Processing - Estimation Theory, Estimation Theory" Kay S. M. 0-13-042268-1   Prentice Hall, 1993 Yes  English 
    Lecture slides "Statistical Signal Processing"         Yes  English 

    Prerequisites
    Code Course Credits M/R
    MAT-33310 MAT-33310 Statistics 3-6 Recommendable
    SGN-1200 SGN-1200 Signal Processing Methods 4 Recommendable

    Prequisite relations (Sign up to TUT Intranet required)

    Additional information about prerequisites
    Basic tools of statistics and general knowledge of random variables are required.

    Remarks

  • Partial passing of course must be in connection with the same round of implementation.

  • The course is suitable for postgraduate studies.

  • Course will not be lectured in the academic year 2007-2008.

    • Distance learning

  • ITC utilized during the course

    • - In information distribution via homepage, newsgroups or mailing lists, e.g. current issues, timetables
    - In compiling exercise, group or laboratory work
    - In distributing and/or returning exercise work, material etc

  • Estimate as a percentage of the implementation of the course

    • - Contact teaching: 35 %
    - Distance learning: 0 %
    - Proportion of a student's independent study: 65 %

    Scaling
    Methods of instructionHours
    Lectures 48
    Exercises 66
    Assignments 15

    Other scaledHours
    Exam/midterm exam 3
    Total sum 132

    Principles and starting points related to the instruction and learning of the course

  • The course consists of lectures and practical exercise sessions. Students are encouraged to ask questions both during/after lectures and exercise discussions.

    • Correspondence of content
    8001403 Statistical signal processing

    Course homepage

    Last modified 30.01.2007
    Modified bySari Peltonen