At the end of this course, the students; 1) Know the statistical decision theory concepts and criteria for the decision. 2) Know the basic structure of the transformations and applies them. 3) Learn the detection of signal with noises. 4) Know the basic models for signal detection. 5) Know and apply the methods used for parameter estimation. 6) Learn the Wiener and Kalman filters.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
Detection, estimation and filter theory with applications in Communications and Signal Processing. MAP and MSE detection theories, Wiener filtering.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Detection, estimation and filter theory
2nd Week
Detection, estimation and filter theory
3rd Week
Detection, estimation and filter theory
4th Week
Applications in Communications and Signal Processing
5th Week
Applications in Communications and Signal Processing
6th Week
Applications in Communications and Signal Processing
7th Week
Probability theory
8th Week
Midterm Exam
9th Week
Probability theory
10th Week
MAP and MSE detection theories
11th Week
MAP and MSE detection theories
12th Week
MAP and MSE detection theories
13th Week
Wiener filtering
14th Week
Wiener filtering
RECOMENDED OR REQUIRED READING
1. M.D. Srinath, P.K. Rajasekaran, R. Viswanathan, Introduction to statistical signal processing with applications Englewood Cliffs, N.J. Prentice Hall, c1996 2. H. Vincent Poor, An introduction to signal detection and estimation, 2nd Ed., New York. Springer-Verlag, c1994 3. Harry L. Van Trees, Detection, estimation, and modulation theory part 1, New York, Wiley 1968, 2001
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Lecture,Presentation,Questions/Answers
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
30
Assignment
2
20
Total(%)
50
Contribution of In-term Studies to Overall Grade(%)
50
Contribution of Final Examination to Overall Grade(%)
50
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
3
3
Preparation for Quiz
0
0
0
Individual or group work
14
8
112
Preparation for Final exam
1
40
40
Course hours
14
3
42
Preparation for Midterm exam
1
30
30
Laboratory (including preparation)
0
0
0
Final exam
1
3
3
Homework
2
35
70
Total Workload
300
Total Workload / 30
10
ECTS Credits of the Course
10
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)