Ph.D. Level Courses
Detection and Estimation Theory
Objectives
This course gives a comprehensive introduction to estimation and detection (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 SystemsRegistration
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 Jan 31, 2010.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 12 seminars/meetings. Course start Monday February 15, 2010, at 9.15 am (room: Hammingrummet).
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) are here
- Additional homework problems are 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
- 5 ECTS version:
- Active seminar participation.
- Solve 40% of the problems for each homework assignment.
- Oral exam, when necessary.
- 10 ECTS version:
- Active seminar participation
- Solve 75% of the problems for each homework assignment.
- Present one or more problem per homework seminar on the board.
- Oral exam, when necessary.
Page responsible:
Erik Larsson
Last updated: 2019 07 29 15:48