# Department of Electrical and Electronics Engineering

Course Details

#### ELE771 - Spectral Estimation

2023-2024 Summer term information
The course is not open this term
ELE771 - Spectral Estimation
 Program Theoretýcal hours Practical hours Local credit ECTS credit PhD 3 0 3 10
 Obligation : Elective Prerequisite courses : - Concurrent courses : - Delivery modes : Face-to-Face Learning and teaching strategies : Lecture, Question and Answer, Problem Solving Course objective : Successful students are expected to gain knowledge of basic spectral estimation methods used for analysis of stochastic processes and signals. Learning outcomes : A student completing the course successfully Recognizes spcetral estimation problems, Models the encountered problems in suitable forms, Knows which algorithms can be used to solve the problem established, knows advantages and disadvantages of these algorithms, Applies the techniques and algorithms learnt in the class in projects, Has the adequate knowledge to follow and understand advanced up-to-date algorithms. Course content : Review of probability and stochastic processes. Periodogram and Blackman-Tukey spectral estimation. Autoregressive (AR), moving average (MA) and autoregressive-moving average (ARMA) spectral estimation. Minimum-variance spectral estimation. Sinusoidal Parameter Est. Bispectrum and polyspectrum. Spectral estimation of Nonstationary signals. Array processing. References : 1. Spectral Analysis of Signals, P. Stoica and R. Moses. Pearson.; 2. Modern Spectral Estimation, S. Kay. Prectice-Hall.; 3. Digital Spectral Analysis, L. Marple. Prentice-Hall.; 4. Lecture Notes.
Course Outline Weekly
Weeks Topics
1 Review Of Probability.
2 Power Spectral Density.
3 Periodogram , Avg. Periodogram, Blackman-Tukey, Welch Methods.
4 Parametric Modelling, Linear Prediction.
5 Levinson Algorithm, Maximum Entropy.
6 Sinusoids in Noise.
7 Autocorrelation, Covariance, Modified Cov. Methods, Burg Algoritm
8 Durbin Method(MA) , ARMA spectral Estimation.
9 Term Exam.
10 Model Order Estimation, Minimum Variance Sp. Est., Filterbank.
11 Sinusoidal Parameter Est., Pisarenko, MUSIC, ESPRIT.
12 Higher Order Spcetrum (Bispectrum)
13 Nonstationary Spectral Estimation (Wigner D., Wavelet Tr., Evolutionary Sp.)
14 Array Processing.
15 Final Exam.
16 Final Exam.
Assessment Methods
Course activities Number Percentage
Attendance 0 0
Laboratory 0 0
Application 0 0
Field activities 0 0
Specific practical training 0 0
Assignments 8 12
Presentation 0 0
Project 1 8
Seminar 0 0
Quiz 0 0
Midterms 1 30
Final exam 1 50
Total 100
Percentage of semester activities contributing grade success 50
Percentage of final exam contributing grade success 50
Total 100
Course activities Number Duration (hours) Total workload
Course Duration 14 3 42
Laboratory 0 0 0
Application 0 0 0
Specific practical training 0 0 0
Field activities 0 0 0
Study Hours Out of Class (Preliminary work, reinforcement, etc.) 14 9 126
Presentation / Seminar Preparation 0 0 0
Project 1 15 15
Homework assignment 8 6 48
Quiz 0 0 0
Midterms (Study duration) 1 30 30
Final Exam (Study duration) 1 40 40