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. 




Asst. Prof. Dr. Tansu Filik

EEM - 214

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


Midterm-I (take home): %15, Midterm-II: %20 (in class), Final: %35, Homeworks: %10, Project: %20


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






Background materials:

·         book, An Introduction to Statistical Signal Processing

·         (fast review for discrete random processes)

·         book, Intuitive Probability and Random Processes Using MATLAB)






Notes on Gaussian distribution by Dr. C. Candan:


Week-I (9th Feb.)


ppt (presentation)

Week-II (16th Feb.)

Review of Random Variables and Random Processes

Lecture notes

Matlab Exercise for random variables

Week-III(23rd Feb.)

Review of Random Variables and Random Processes

There will be no lessons on 23rd February.

HW-1 is submitted.

Week-IV (2nd Mar.)

Review of Random Variables and Random Processes

HW1 (due:9th March)

Lecture notes

Week-V (9th Mar.)

Minimum variance unbiased estimation (MVU)

Lecture notes

Week-VI (16th Mar.)

Cramer Rao Lower Bound (CRLB), Linear Models

Lecture notes

HW-2 (Due:30th March) (3.3/3.4/3.5/3.6)

Week-VII (23rd Mar.)

Linear Models, Best Linear Models

Note: Please visit Projects page! (Project proposal deadline: 6th April )

Lecture notes

Lecture notes

Week-VIII (30th Mar.)

Maximum Likelihood Estimation

Lecture notes

Week-IX (6th April)

Least Square Estimation

Lecture notes

Week-X (13th April)

Midterm Exam (in class)

in E3 at 10:00 am

Week-XI (20th April)

Bayesian estimation

Take home exam (midterm-II)

Lecture notes

Take home exam-1

(RSS data generation code example)

Week-XII (27th April)

Bayesian Estimation

Lecture notes


Important Dates:

· Extended deadline for take home exam-1: 5th May 2017

· Important dates for Project report submissions


Week-XIII (4th May)

Wiener and Kalman Filtering

Lecture notes

Week –XIV (11th May)

Kalman Filtering

Lecture notes

Final take home exam