Valid for: 2012/13
Decided by: Education Board 1
Date of Decision: 2012-03-27
Elective for: C4, D4, E4, F4, F4-ssr, F4-bm, I4, Pi4, Pi4-ssr
Language of instruction: The course might be given in English
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.
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.
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 of the different parts of the examination.
Parts
Code: 0113. Name: Computer Exercises.
Credits: 3. Grading scale: UG. Assessment: Computer exercises with reports
Code: 0213. Name: Home Assignments.
Credits: 4,5. Grading scale: UG. Assessment: Home assignments
Required prior knowledge: FMS051 Time series analysis
The number of participants is limited to: No
The course might be cancelled: If the number of applicants is less than 16.
The course overlaps following course/s: MASM26
Course coordinator: Prof Andreas Jakobsson, andreas.jakobsson@matstat.lu.se
Course coordinator: Prof Maria Sandsten, sandsten@maths.lth.se
Director of studies: Studierektor Anna Lindgren, studierektor@matstat.lu.se
Course homepage: http://www.maths.lth.se/matstat/kurser/masm26/
Further information: The course is also given at the Science faculty with the code MASM26