Information Package / Course Catalogue - Adnan Menderes University

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Detection & Estimation Theory

Course Code	: EEE532
Course Type	: Area Elective
Couse Group	: Second Cycle (Master's Degree)
Education Language	: Turkish
Work Placement	: N/A

Theory	: 3
Prt.	: 0
Credit	: 3
Lab	: 0
ECTS	: 6

Objectives of the Course

The main objective of this course is to introduce the principles of statistical detection and estimation theory to the students. In this context, detection of both deterministic and random signals will be studied. Using approaches like Bayesian and maximum likelihood, parameter estimation from noisy signals will be realized.

Course Content

Hypothesis testing: Bayesian, minimax and Neyman-Pearson approaches, Detection of deterministic and random signals, General Minimum Variance Unbiased Estimation, Cramer Rao Lower Bound, Maximum likelihood estimation.

Name of Lecturer(s)

Learning Outcomes

1.	To be familiar with detection concept and to apply detection theory in signal processing
2.	To identify detection problems for deterministic and random signals
3.	To be able to apply the Bayesian, minimax or Neyman-Pearson methods to detect signals
4.	To be able to construct an estimation problem and specify the likelihood function
5.	To be able to design minimum variance unbiased and maximum likelihood estimators

Recommended or Required Reading

1.	S. M. Kay: Fundamentals of Statistical Signal Processing, Vol. 1, Estimation Theory.
2.	S. M. Kay: Fundamentals of Statistical Signal Processing, Vol.2 Detection Theory.
3.	An Introduction to Signal Detection and Estimation, H. Vincent Poor

Weekly Detailed Course Contents

Week 1 - Theoretical

Detection Theory in Signal Processing. The Detection Problem

Week 2 - Theoretical

Statistical Decision Theory, Neyman-Pearson Theorem

Week 3 - Theoretical

Minimum Bayes Risk Detector - Binary Hypothesis.

Week 4 - Theoretical

Deterministic Signals with Unknown Parameters

Week 5 - Theoretical

Random Signals with Unknown Parameters

Week 6 - Theoretical

Unknown Noise Parameters, White Gaussian Noise. Colored WSS Gaussian Noise.

Week 7 - Theoretical

GLRT and Rao Test

Week 8 - Theoretical

Bayesian estimation theory, Midterm exam

Week 9 - Theoretical

Minimum variance unbiased estimators

Week 10 - Theoretical

Cramer Rao Lower Bound (CRLB). CRLB for Signals with White Gaussian Noise

Week 11 - Theoretical

Maximum likelihood estimation, Least squares estimation

Week 12 - Theoretical

Minimum mean square error (MMSE) estimation,

Week 13 - Theoretical

Maximum a posteriori probability (MAP) estimation

Week 14 - Theoretical

Wiener and Kalman filtering

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Attending Lectures	1	%5
Assignment	2	%10
Midterm Examination	1	%15
Final Examination	1	%70

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	1	3	56
Assignment	2	14	0	28
Individual Work	14	3	0	42
Midterm Examination	1	10	2	12
Final Examination	1	10	2	12
TOTAL WORKLOAD (hours)				150

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7
OÇ-1	4	4	4	4	3	3	5
OÇ-2	4	4	5	4	3	3	4
OÇ-3	5	3	5	3	3	3	4
OÇ-4	4	4	4	4	3	3	4
OÇ-5	5	4	5	3	3	3	5

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