Course syllabus

Stationär och icke-stationär spektralanalys
Stationary and Non-stationary Spectral Analysis

FMSN35, 7,5 credits, A (Second Cycle)

Valid for: 2015/16
Decided by: Education Board B
Date of Decision: 2015-04-16

General Information

Elective for: BME4, C4, D4, E4-ss, F4, F4-ss, I4, Pi4-ssr, Pi4-biek
Language of instruction: The course will be given in English on demand


This course is aimed at those who want wo broaden and deepen their knowledge in statistical signal processing and expand their toolkit with more advanced techniques. It lies on the border between statistics and signal processing and builds on the classical non-parametric methods that are wellknown and taught in, e.g. Stationary stochastic processes or Optimal signal processing. Since these methods are not always sufficient we need more advanced techniques in many application areas, e.g. communications or medicine.

Hence, the course covers more statistically robust methods that have become increasingly used in resent years, e.g. time-frequency analysis, which is a modern method for analysis of non-stationary signals and processes. The research in this area has expanded during the last 20 years, making this a commonly used tool.

Many applications will be presented in the course and the participants will work with real world data.


Learning outcomes

Knowledge and understanding
For a passing grade the student must


Competences and skills
For a passing grade the student must


Basic definitions. Extended studies of AR (auto regressive), MA (moving average) och ARMA-processes. Linespectra and parametric estimation methods. Noise-space based techniques. Non-parametric spectral estimators, data-adaptive techniques and multitaper methods. Non-uniform sampling. Orientation of circular and non-circular processes. Spatial spectral analysis. Non-stationary processes. Spectrogram. Wigner-Ville distribution. Cohen class. Ambiguity spectrum. Multitaper techniques for non-stationary signals. Orientation about bispectrum.

Examination details

Grading scale: TH
Assessment: Approved exercises and project reports as well as participation in all compulsary parts. The final grade is given by a summary of the results on the assignments and exercise reports.

Code: 0116. Name: Computer Exercises.
Credits: 3. Grading scale: UG. Assessment: Computer exercises with reports
Code: 0216. Name: Assignments Part 1.
Credits: 1. Grading scale: UG. Assessment: The first home assignment
Code: 0316. Name: Assignments Part 2.
Credits: 3,5. Grading scale: UG. Assessment: The rest of the home assignments


Admission requirements:

Required prior knowledge: FMS051 Time series analysis
The number of participants is limited to: No
The course overlaps following course/s: MASM26

Reading list

Contact and other information

Course coordinator: Prof Andreas Jakobsson,
Course coordinator: Prof Maria Sandsten,
Director of studies: Studierektor Anna Lindgren,
Course homepage:
Further information: The course is also given at the Science faculty with the code MASM26