EEM547 Fundamentals of Detection and Estimation

The objective of this course is to present the theory and applications of statistical signal processing to detection and estimation of signal parameters in noise. A solid background in signal processing, probability and random processes, and linear and matrix algebra is needed. 

Instructor:

Office:

e-mail:

Assoc. Prof. Dr. Tansu Filik

EEM - 214

tansufilik@anadolu.edu.tr

Course Outline:

1.      Introduction to signal detection and estimation

2.      Review of the theory of random variables and random signals

3.      Classical estimation theory, general minimum variance unbiased (MVU) estimation

4.      Cramer-Rao Lower Bound (CRLB)

5.      Linear models and best linear unbiased estimators

6.      Maximum likelihood estimation

7.      Least squares estimation

8.      Bayesian estimation

9.      Wiener and Kalman filtering

10.  Classical detection theory

11.  Detection in Gaussian and non-Gaussian noise

Grading:

Midterm-I (take home): %15, Midterm-II: %20 (in class), Final: %35, Homework’s: %10,

Project: %20

Textbooks:

1) Steven M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall,

2) Steven M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, Prentice Hall,

3) Hary L. Van Trees, Detection, Estimation, and Modulation Theory, Part 1, Wiley-Interscience

Reference Books:

·         Vincent Poor, An Introduction to Signal Detection and Estimation, Springer,

·         Charles W. Therrien, Discrete Random Signals and Statistical Signal Processing, Prentice Hall,

·         Monson H. Hayes, Statistical Digital Signal Processing and Modelling, Wiley

 

Lectures

Subject

Links

 

Background materials:

·         book, An Introduction to Statistical Signal Processing

·         (fast review for discrete random processes)

·         book, Intuitive Probability and Random Processes Using MATLAB)

 

link1

link2

link3

 

Notes on Gaussian distribution by Dr. C. Candan:

link4

Week-I (8th Feb.)

Introduction, ppt (presentation)

There will be no lecture on 8th February

Lectures will start on 15th February

Week-II (15th Feb.)

Introduction,

Review of Random Variables and Random Processes

Lecture note

Week-III (22rd Feb.)

Review of Random Variables and Random Processes

Lecture note

Week-IV (1st Mar.)

Minimum variance unbiased (MVU) estimation

HW-1 is submitted

Lecture note

Week-V (8th Mar.)

Cramer-Rao Lower Bound (CRLB)

Lecture notes

HW-2: 3.3, 3.5, 3.6, 3.11 (the questions from textbook-1)

Week-VI (15th Mar.)

Linear models and best linear unbiased estimators

Lecture notes

Week-VII (22nd Mar.)

Best linear unbiased estimators (BLUE)

Lecture notes

HW-3: 4.1, 4.2, 4.4 (from textbook-1)

Due: 5th April

Week-VIII (29th Mar.)

Midterm Examination (in class – 29th March at 10:00 am)

 

1st Midterm exam will be held on 29th March in class at 10:00 am.

important announcement

You should propose a project due to April 5. Please visit Project page for details.

Projects

Week-IX (5th April)

Maximum likelihood estimation, Least Square Estimation

Lecture notes

HW-4: 7.10, 7.13, 8.3, 8.5  (from textbook-1)

Due: 12th April

Week-X (12th April)

Least Square Estimation, Classical detection theory

Lecture notes

 

Week-XI(19th April)

Classical detection theory, Bayesian Estimation

Lecture notes

Lecture notes-2

 

Week-XII (26th April)

Wiener Filtering

Lecture Notes

Week-XIII (3rd May)

 

There will be no lecture on 3rd May

Week-XIV (10th May)

Kalman Filtering

Lecture Notes

Take Home Exam-1

Week-XV (17th May)

Submit project final report and present your work in class (last week- May 10 May 17)

Present your project (10 7 minutes presentation) in Class (17 th May 10:00 09:30 am in department’s meeting room

 

 

Take Home Exam-2