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Ph.D. Level Courses

Detection and Estimation Theory

Erik G. Larsson
Erik G. Larsson

Objectives

This course gives a comprehensive introduction to decision-making based on observations of discrete-time and continuous-time signals. The course has applications in many areas, for example communications and radar.

Instructor

Prof. Prof. Erik G. Larsson, ISY/Communication Systems

Registration

The course is open to students enrolled in a Ph.D. program at Linkoping University/ISY. External participants upon request. If you have interest to participate, please register by sending an email to the instructor, by Feb. 22nd, 2008.

Prerequisites

Good knowledge of linear algebra, probability, and stochastic processes. General mathematical maturity.

Course outline (tentative)

  • Binary and M-ary hypothesis testing
  • Detection theory: Neyman-Pearson, ROC, Bayesian criteria
  • Estimation theory: classical estimation, maximum likelihood, Cramer-Rao lower bound, Bayesian estimation, MMSE
  • Composite hypothesis testing, model order selection
  • General Gaussian models
  • Representation of continuous-time waveforms and noise (Karhunen-Loeve expansion)
  • Detection and parameter estimation of signals in additive Gaussian noise

Schedule

The course consists of 10 seminars/meetings. Course start is March 3rd, 10-12 (venue: conference room "Filtret").

A course schedule (with reading and homework) can be found here (this will be continuously updated during the course).

Literature

  • H. van Trees, "Detection, estimation and modulation theory", Wiley, 1967. (Paperback edition ISBN 0-471-095176, 2001.)
  • Course notes (will be continuously updated during the course) PS PDF
  • Additional homework problems here
  • Supplementary material handed out during the course and/or available from this webpage

Credits

The course is estimated to be worth 10 ECTS credits.

Examination

  • Active seminar participation.
  • Homework problems, to be solved individually and handed in at each meeting. (8 assignments in total. Each assignment graded as follows. Less than 50% solved correctly: 0 marks. Between 50-80%: 1 mark. More than 80%: 2 marks. Solution not submitted on time: 0 marks. Threshold for passing the course: 12 marks.)
  • Oral exam, when necessary.

Note

  • There are PostScript files here. You may need GhostView.

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Last updated: 2019 07 29   15:48


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