Study Guide@lith

Linköping Institute of Technology

Valid for year : 2017
TSKS15 Detection and Estimation of Signals, 6 ECTS credits.
/Detektion och estimering av signaler/

For:   D   I   Ii   IT   MMAT   SY   Y  


Prel. scheduled hours:
Rec. self-study hours: 160

  Area of Education: Technology

Main field of studies: Electrical Engineering

  Advancement level (G1, G2, A): A

After completed course the student should:
  • with adequate terminology, in a well-structured manner and logically coherent, be able to describe and conduct simpler calculations that relate to classical and Bayesian estimation and detection theory, specifically the Neyman-Pearson theorem, error probabilities, decision regions, maximum-likelihood, linear and nonlinear models, Fisher information, Cramer-Rao bound, circularly symmetric noise, noise whitening, MMSE and LMMSE, GLRT, model order selection, coherent and non-coherent detection, composite hypothesis testing and nuisance parameters and basis expansions of waveforms in continuous time
  • be able to describe, apply and implement in a conventional programming language, and show engineering understanding of, the theory and models used in the course

Prerequisites: (valid for students admitted to programmes within which the course is offered)
Linear algebra, probability theory, and a course similar to Signals, Information and Communications.

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.

Lectures, problem classes and computer laboratory work. Individual (inclass) oral examination of laboratory work.

Course contents:
Binary hypothesis tests, Neyman-Pearson theorem, error probability. M-ary detection problems. Bayes cost, minimum probability of error. Nuisance parameters. Classical estimation: Maximum-likelihood. Cramer-Rao bound, Slepian-Bang's formula, efficiency. Linear, vector-valued models with Gaussian noise. Non-linear models. Noise whitening, complex-valued data, Gaussian noise, circularly symmetric noise. Bayesian estimation: MMSE and LMMSE. Composite hypothesis testing: GLRT and Bayesian approach, model selection. Performance calculations, asymptotic properties of estimators. Applications to amplitude and phase estimation, frequency estimation, angle-of-arrival estimation, time-of-arrival estimation, source localization, coherent and non-coherent detection of waveforms.

Course literature:
S. Kay, Statistical Signal Processing: Estimation Theory och Statistical Signal Processing: Detection Theory, Prenticeā€Hall.

A written examination
Laboratory work

Course language is Swedish/English.
Department offering the course: ISY.
Director of Studies: Klas Nordberg
Link to the course homepage at the department

Linköping Institute of Technology


Contact: TFK ,
Last updated: 03/31/2017