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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for Course FMSN35F Stationary and Non-stationary Spectral Analysis

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General
  • FMSN35F
  • Active
Course Name
  • Stationary and Non-stationary Spectral Analysis
Course Extent
  • 7.5
Type of Instruction
  • Course given jointly for second and third cycle
  • FMSN35
Administrative Information
  • 7152 (Centre of Mathematical Sciences / Mathematical Statistics)
  • FMSN35
  •  -02-27
  • FN1/Anders Gustafsson

Current Established Course Syllabus

General
  • English
  • Every other spring semester
Aim
  • This course is aimed at those who want to broaden and deepen their knowledge in statistical signal processing and expand their toolkit with more advanced techniques. The course lies on the border between statistics and signal processing and builds on the classical non-parametric methods that are well-known and taught in courses like 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. communication or medicine. Hence, the course covers more statistically robust methods that have become increasingly used in recent 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.
Contents
  • 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.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • Be able to interpret and understand parametric and non-parametric spectral estimation methods.
    Be able to interpret and understand spatial spectral analysis and classical estimation techniques of directions.
    Be able to interpret and understand time-frequency analysis and classical estimation techniques of non-stationary spectra.
Competences and Skills
  • For a passing grade the doctoral student must
  • Be able to estimate classical parametric and non-parametric spectral estimates.
    Be able to estimate spectra of non-uniformly sampled sequences.
    Be able to use classical time-frequency methods for estimation.
Judgement and Approach
  • For a passing grade the doctoral student must
Types of Instruction
  • Lectures
  • Exercises
  • Project
Examination Formats
  • Written report
  • Written assignments
  • Seminars given by participants
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
Selection Criteria
Literature
  • Sandsten, M.: Lecture notes: Time-frequency analysis. 2011.
Further Information
Course code
  • FMSN35F
Administrative Information
  •  -02-27
  • FN1/Anders Gustafsson

All Established Course Syllabi

1 course syllabus.

Valid from First hand in Second hand in Established
Spring 2014 2014‑01‑10 10:47:57 2014‑01‑10 12:23:30 2014‑02‑27

Current or Upcoming Published Course Occasion

No matching course occasion was found.

All Published Course Occasions

2 course occasions.

Course code ▽ Course Name ▽ Division ▽ Established ▽ Course syllabus valid from ▽ Start Date ▽ End Date ▼ Published ▽
FMSN35F Stationary and Non-stationary Spectral Analysis Mathematical Statistics 2017‑12‑20 Spring 2014 2018‑01‑15 2018‑03‑10 2017‑12‑20
FMSN35F Stationary and Non-stationary Spectral Analysis Mathematical Statistics 2019‑12‑06 Spring 2014 2020‑01‑15 2020‑03‑09 2019‑12‑06

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