Course syllabus for Random signals analysis

The course syllabus contains changes
See changes

Course syllabus adopted 2019-02-07 by Head of Programme (or corresponding).

Overview

  • Swedish nameAnalys av stokastiska signaler
  • CodeMVE136
  • Credits7.5 Credits
  • OwnerMPCOM
  • Education cycleSecond-cycle
  • Main field of studyElectrical Engineering
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 13120
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0111 Examination 6 c
Grading: TH		6 c
  • 30 Okt 2020 am J
  • 04 Jan 2021 pm J
  • 16 Aug 2021 pm J
0211 Laboratory 1.5 c
Grading: UG		1.5 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

A first course in probability for engineering and science students. A first course in signals, systems, and transforms.

Aim

The purpose of the course is to provide the students with the theoretical framework fundamental to the processing of signals with random variation. Starting from basic probability the course proceeds to a thorough study of models for stochastic processes which are relevant in processing of random signals, and gives techniques for manipulating and study of these signals. Practical methods for random signal analysis and filtering are also included. The level should be such that the student should be able to take an active part in designing and optimizing engineering systems involving random signals.

Learning outcomes (after completion of the course the student should be able to)

  • define and explain fundamental probabilistic tools used in the design and analysis of communication systems, with emphasis on multidimensional joint distributions, the Gaussian one in particular, and on conditional probabilities and expectations.
  • identify basic models of random processes and explain their use for the design of components in communication systems and analysis of their effect on system performance. These models include the Poisson process, Markov processes, Gaussian processes, white noise, and stationary stochastic processes.
  • use wide-sense stationary processes for modeling systems involving random signals and noise. In particular the students should be familiar with the important classes of AR, MA and ARMA processes.
  • estimate second-order characteristics from data, including non-parametric and parametric estimation of the power spectral density, and understand the statistical properties of these estimates.
  • estimate linear filters from noisy measurements of the output by linear regression techniques.
  • explain the mathematical techniques for design of optimal linear systems for signal processing, with emphasis on matched filtering and the Wiener filter.

Content

Background
  • Review of Basic Probability and Random Variables
  • Multiple Random Variables. Conditional Distributions. Conditional Expectation.
  • Multidimensional Gaussian Distribution.
Random Processes
  • Definition of a Random Process. Autocorrelation Functions.
  • Wiener process, White Gaussian Noise, Poisson Process. Markov Process.
  • Wide-Sense Stationary Random Processes. Spectral Representation. AR, MA and ARMA Processes.
  • Analysis and Processing of Random Signals Through a Linear System. Cross-Correlation and Cross-Spectrum.
Statistical Signal Processing
  • Non-Parametric Spectral Estimation. Windowing and Frequency Resolution. Welch and Blackman-Tukey methods.
  • Parametric Spectral Estimation. Yule-Walker method.
  • Optimum Linear Systems: Matched Filtering and Wiener Filters.
  • Prediction, Filtering and Smoothing.
  • System Identification with Application to Channel Estimation.

Organisation

Lectures, exercise classes and supervised laborations.

Literature

Scott Miller och Donald Childers (2012): Probability and Random Processes Second Edition, Academic Press. 

It is also quite possible to use the first edition of the book from 2004.åäö

Examination including compulsory elements

The course is examined by means of two mandatory laborations and a written exam. The final grade on the course is the same as that on the written exam.evaluation is based on the results from the computer laborations, the home assignments, and the written final examination.

The course syllabus contains changes

  • Changes to examination:
    • 2020-09-30: Grade raising No longer grade raising by GRULG