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EQ2810 Estimation Theory, Accelerated Program Course 6.0 credits

Estimeringsteori, forskarförberedande

This is an introductory course to statistical estimation theory given from a signal processing perspective. The course covers fundamental concepts such as sufficient statistics, the Rao-Blackwell theorem and the Cramer-Rao lower bound on estimation accuracy. Furthermore, the most common estimation methods are treated, including maximum likelihood, least-squares, minimum variance and Bayesian estimation.

This is a graduate level course that can be taken by undergraduate students who are admitted. There are two versions of the course, 6 and 12 ECTS.

  • Educational level

    Second cycle

  • Academic level (A-D)

    D

  • Subject area

  • Grade scale

    A, B, C, D, E, FX, F

Course offerings

Autumn 13 for programme students

Learning outcomes

This is an introductory course to statistical estimation theory given from a signal processing perspective. The aim is to provide the basic principles and tools which are useful to solve many estimation problems in signal processing and communications. It will also serve as the necessary prerequisite for more advanced texts and research papers in the area. The course will cover fundamental concepts such as sufficient statistics, the Rao-Blackwell theorem and the Cramer-Rao lower bound on estimation accuracy. Furthermore, the most common estimation methods are treated, including maximum likelihood, least-squares, minimum variance, method of moments and Bayesian estimation. The course assumes some familiarity with basic matrix theory and statistics.

Course main content

Introduction, minimum variance estimation, Cramer-Rao bound. General minimum variance and best linear unbiased estimation. Maximum likelihood estimation, least squares, method of moments, Bayesian estimation. Extensions for complex data and parameters.

Eligibility

For single course students: 180 credits and documented proficiency in English B or equivalent

Prerequisites

2E1340 Digital Signal Processing grade 4 or 5 and the permission of the examiner.
2E1360/2E5320 Matrix Algebra, accelerated program is recommended but not required.

Literature

"Fundamentals of Statistical Signal Processing: Estimation Theory," Kay, Steven M. ISBN 0133457117.

Examination

  • LAB1 - Laboratory Work, 1.5 credits, grade scale: P, F
  • TEN1 - Examination, 4.5 credits, grade scale: A, B, C, D, E, FX, F

Requirements for final grade

Attending the lectures is mandatory
Homework assignments oral examination (not required if the homeworks are correct) project assignment.

Offered by

EES/Signal Processing

Contact

Peter Händel

Examiner

Peter Händel

Supplementary information

Given every second year. Given period 1 11/12.

Version

Course plan valid from: Autumn 07.
Examination information valid from: Autumn 07.