Master’s studies

Syllabus for Spectral Processing of Signals

Spektral signalbehandling

Syllabus

  • 5 credits
  • Course code: 1RT605
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Technology A1F
  • Grading system: Fail (U), 3, 4, 5.
  • Established: 2011-03-07
  • Established by: The Faculty Board of Science and Technology
  • Revised: 2017-05-03
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 27, 2017
  • Entry requirements: 120 credits including Linear Algebra II, Probability and Statistics, Signals and Systems, Scientific Computing II, and Signal Processing or Automatic Control II.
  • Responsible department: Department of Information Technology

Learning outcomes

The course reviews classical and modern methods and algorithms for computer-based spectral analysis of signals. Also, it gives an overview of various applications in communications, systems engineering, radar, and biomedicine. After the course, the student will:


  • understand the spectral estimation problem and the meaning of spectrum

  • understand the differences between non-parametric and parametric approaches to the spectral estimation problem

  • master several non-parametric methods for spectral estimation, both periodogram and correlogram-based methods as well as data adaptive filter-bank methods, and be able to use this knowledge to solve real-world problems

  • master several parametric methods for estimation of line spectra as well as rational spectra, and be able to use this knowledge to solve real-world problems

  • be able to decide what methods ( for example, parametric or non-parametric) are suitable for a specific problem

  • be able to solve spectral estimation problems and to visualise their solutions using the MATLAB software

  • be prepared to use the tools of spectral analysis of signals for solving practical problems in a diversity of areas such as control system modelling, wireless communication systems, radar and sonar signal processing, genomic data mining, image processing, magnetic resonance spectroscopy, acoustic imaging, biomedical signal processing, economic, geophysical, astronomic (etc.) data processing, Internet traffic analysis, and so forth.

Content

Basic definitions and overview of the spectral estimation problem. The periodogram and correlogram methods. Improved methods based on the periodogram. Filter-bank methods. Parametric methods for rational spectra and for line spectra. Review of selected applications.

Instruction

Lectures and computer-based exercises

Assessment

Graded homework assignments and passed computer exercises.

Reading list

Applies from: week 27, 2017

  • Stoica, Petre; Moses, Randolph Spectral analysis of signals

    Upper Saddle River, N.J.: Pearson Prentice Hall, cop. 2005

    Find in the library

    Mandatory