Statistical Signal Processing (EE422) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Statistical Signal Processing	EE422	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
EE303 ve EE213

Course Language	English
Course Type	Elective Courses
Course Level	Natural & Applied Sciences Master's Degree
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	To present fundamental skills that target the analysis of signals with stochastic properties to electrical and electronic engineering students.
Course Learning Outcomes	The students who succeeded in this course; Ability to characterize an estimator Ability to design statistical DSP algorithms to meet desired needs Ability to apply vector space methods to statistical signal processing problems Ability to understand Wiener filter theory and design discrete and continuous Wiener filters Ability to understand Kalman Filter theory and design discrete Kalman filters Ability to use computer tools (such as Matlab) in developing and testing stochastic DSP algorithms Ability to complete a term project
Course Content	Introduction to random process, detection and estimation theory, maximum variance unbiased estimation, Cramer-Rao lower bound, general minimum variance unbiased estimation, best linear unbiased estimation, maximum likelihood estimation, Least square methods of estimation, method of moments: second moments analysis, Bayesian philosophy and Bayesian

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Introduction,overview of history and applications of detection and estimation. Review of the requisite mathematical concepts, including concepts in matrix, probability, and statistical analysis.	Glance this week’s topics from the lecture
2	Introduction and Review	Review last week and glance this week’s topics from the lecture
3	Sufficiency and Minimum Variance Unbiased (MVUB) Estimators	Glance this week’s topics from the lecture
4	Neyman-Pearson Detectors: Classifying Tests, The Testing Binary Hypothesis, Neyman-Pearson Lemma, Binary Communication, Matched Filters	Glance this week’s topics from the lecture
5	Neyman-Pearson Detectors	Review last week and glance this week’s topics from the lecture
6	Bayes Detectors: Bayes Risks for Hypothesis Testing, Minimax Tests, M-Orthogonal Signals, Likelihood Ratios	Glance this week’s topics from the lecture
7	Bayes Detectors	Review last week and glance this week’s topics from the lecture
8	Maximum Likelihood Estimators: Maximum Likelihood Principle, The Fisher Matrix and Cramer-Rao Bound, The Linear Statistical Model, Maximum Likelihood Identification of a Signal Subspace	Glance this week’s topics from the lecture
9	Maximum Likelihood Estimators	Review last week and glance this week’s topics from the lecture
10	Bayes Estimators: Bayes Risk for Parameter Estimation, Computing Bayes Risk Estimators, Sequential Bayes, The Kalman Filter, The Wiener Filter	Glance this week’s topics from the lecture
11	Bayes Estimators	Review last week and glance this week’s topics from the lecture
12	Minimum Mean-Squared Error (MMSE) Estimators: Conditional Expectation and Orthogonality, Linear MMSE Estimators, Linear Prediction, The Kalman Filter	Glance this week’s topics from the lecture
13	Minimum Mean-Squared Error (MMSE) Estimators	Review last week and glance this week’s topics from the lecture
14	Least Squares	Glance this week’s topics from the lecture
15	Final examination period	Review topics
16	Final examination period	Review topics

Sources

Course Book	1. Statistical Signal Processing:Detection, Estimation and Time Series Analysis, Louis L. Scharf, Addison-Wesley, 1991.
Other Sources	2. Fundamentals of Statistical Signal Processing: Estimation Theory, S. M. Kay, Prentice Hall, 1993.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	14	15
Presentation	-	-
Project	-	-
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	2	40
Final Exam/Final Jury	1	30
Toplam	17	85

Percentage of Semester Work	70
Percentage of Final Work	30
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems.					X
2	Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering.					X
3	Ability to use modern engineering tools, techniques and facilities in design and other engineering applications.					X
4	Graduating researchers active on innovation and entrepreneurship.
5	Ability to report and present research results effectively.
6	Increasing the performance on accessing information resources and on following recent developments in science and technology.
7	An understanding of professional and ethical responsibility.
8	Increasing the performance on effective communications in both Turkish and English.
9	Increasing the performance on project management.
10	Ability to work successfully at project teams in interdisciplinary fields.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	14	3	42
Presentation/Seminar Prepration
Project
Report
Homework Assignments	2	2	4
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	2	10	20
Prepration of Final Exams/Final Jury	1	20	20
Total Workload			134