Statistical Signal Processing

University of Oulu
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Course overview

 
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ECTS Credits 5   cr
Language of instruction FI.

Finnish, Course can be passed in English.

Timing

Spring, periods 4.

Learning outcomes

1. is able to utilize the generic linear model as a representation for parameter estimation

2. can apply typical deterministic and random parameter estimation methods for different estimation problems

3. is able to determine statistical properties of estimators and make comparisons between them

4. can form a basic state-variable model and utilize Kalman filtering for state estimation

5. is able to apply basic methods of detection theory for solving simple detection problems

6. can implement the learned methods and assess their statistical properties with the Matlab software

Contents

This course provides basic knowledge of statistical signal processing, in particular, estimation theory and its applications in signal processing. Topics: 1. Introduction, 2. Modeling of estimation problems, 3. Least Squares estimation, 4. BLUE-estimation, 5. Signal detection, 6. ML estimation, 7. MS estimation, 8. MAP estimation, 9. Kalman Filter.

Mode of delivery

Face-to-face teaching and homework assignments.

Learning activities and teaching methods

Lectures (24 h), exercises (24 h) and Matlab homework assignments (20 h).

Target group

Computer Science and Engineering students and other Students of the University of Oulu.

Prerequisites and co-requisites

031078P Matrix Algebra, 031021P Probability and Mathematical Statistics

Recommended optional programme components

521337A Digital Filters, 031050A Signal Analysis. These courses provide complementary information on digital signal processing and stochastic signals. The courses are recommended to be studied either in advance or simultaneously.

Recommended or required reading

J. Mendel: Lectures in estimation theory for signal processing, communications and control, Prentice-Hall, 1995. M.D. Srinath, P.K. Rajasekaran, R. Viswanathan: Introduction to Statistical Signal Processing with Applications, Prentice-Hall, 1996, Chapter 3. Lecture notes and exercise material.

Assessment Methods and criteria

The course is passed with intermediate exams or final exam and accepted Matlab exercise.

Read more about assessment criteria at the University of Oulu webpage.

Grading

The course unit utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail.

Work placements

No.


University of Oulu oulun.yliopisto(at)oulu.fi
Tel. +358 294 48 0000
Fax +358 8 553 4112
PL 8000
FI-90014 Oulun yliopisto
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