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TT8111

Signal and Estimation Theory

Choose study year

Assessments and mandatory activities may be changed until September 20th.

Credits 7.5
Level Doctoral degree level
Course start Spring 2026
Duration 1 semester
Language of instruction English
Location Trondheim
Examination arrangement Oral exam

About

About the course

Course content

This course is taught every second year (on even years) if enough students. The course emphasizes general estimation theory with applications on various signal processing problems.

Learning outcome

A. Knowledge:

Student has a

1) detailed knowledge of classical and Bayesian estimation theory and applications of these in signal processing.

2) a detailed understanding of properties of minimum variance unbiased estimators.

3) a detailed understanding of the properties of the maximum likelihood estimator and its use in signal processing.

4) detailed knowledge of least squares and its use in signal processing

B. Skills:

Students can

1) assess the quality of an estimator by its variance and Cramer Rao Lower Bound

2) consider different estimators and choose an appropriate method for a given signal processing purpose.

Learning methods and activities

Lectures, colloquiums, PC assignments and exercises.

Compulsory assignments

  • Exercises

Required previous knowledge

Fundamental knowledge in signal processing, statistics and probability theory.

Course materials

S. M. Kay: Fundamentals of Statistical Signal Processing, Vol. 1, Prentice Hall, 1993.

Credit reductions

Course code Reduction From
TT8309 5 sp Autumn 2011
TTT4240 7.5 sp Autumn 2011
This course has academic overlap with the courses in the table above. If you take overlapping courses, you will receive a credit reduction in the course where you have the lowest grade. If the grades are the same, the reduction will be applied to the course completed most recently.

Subject areas

  • Electronics
  • Telecommunication
  • Technological subjects

Contact information

Course coordinator

Lecturers

Department with academic responsibility

Department of Electronic Systems