Information Package / Course Catalogue
Spectral Estimation
Course Code: EEE544
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

To provide the graduate students the fundamentals of spectral estimation methods used in frequency domain analysis of random signals.

Course Content

Basic concepts: Energy and power spectral densities, properties of power spectral densities, the spectral estimation problem, Nonparametric methods for spectral estimation, Parametric methods for rational spectra, Parametric methods for line spectra, Filter bank methods, Spatial methods for spectral estimation.

Name of Lecturer(s)
Learning Outcomes
1.To know and classify the spectral estimation problems.
2.To know parametric methods for spectral estimation and apply them to real world data.
3.To know nonparametric methods for spectral estimation and apply them to real world data.
4.To perform frequency domain analysis of random signals and systems and interpret the implementation outcomes.
5.To have technical background to understand and follow the up-to-date spectral estimation methods and study based on these methods.
Recommended or Required Reading
1.P. Stoica, R. Moses, Spectral Analysis of Signals, Prentice-Hall, NJ, 2005.
2.S. M. Alessio, Digital Signal Processing and Spectral Analysis for Scientists, Springer, 2016.
Weekly Detailed Course Contents
Week 1 - Theoretical
Basic concepts
Week 2 - Theoretical
Spectral Estimation Problem and Its application
Week 3 - Theoretical
Nonparametric methods for spectral estimation: Periodogram and Its various versions
Week 4 - Theoretical
Parametric methods for spectral estimation: Statistical Models and processes
Week 5 - Theoretical
Parametric methods for spectral estimation: AR model approach
Week 6 - Theoretical
Parametric methods for spectral estimation: MA model approach
Week 7 - Theoretical
Parametric methods for spectral estimation: ARMA model approach
Week 8 - Theoretical
Repeat chapters, Midterm Exam.
Week 9 - Theoretical
Minimum Variance Spectral Estimation
Week 10 - Theoretical
Minimum Variance Spectral Estimation
Week 11 - Theoretical
Maximum Entropy Method
Week 12 - Theoretical
Methods for sinusoidal parameter estimation: Pisarenko, MUSIC, ESPRIT methods
Week 13 - Theoretical
Principal Component Analysis and Its applications
Week 14 - Theoretical
Project presentations
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Quiz1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory143384
Assignment110313
Quiz1224
Midterm Examination115318
Final Examination128331
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
3
3
3
3
4
3
4
OÇ-2
4
4
4
3
5
4
3
OÇ-3
3
3
5
5
5
4
3
OÇ-4
3
3
3
3
4
3
4
OÇ-5
3
3
3
3
4
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026