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389.040 Signal Detection
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2022S, VO, 2.0h, 3.0EC

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture
  • Format: Presence

Learning outcomes

After successful completion of the course, students are able to formulate hypotheses test, to develop analytical solutions, to characterize the performance of these solutions qualitatively and quantitatively, and to apply these capabilities to real-world engineering problems.

Subject of course

  • Introduction: motivation, historical remarks, classification of decision problems, performance metrics
  • Simple hypothesis tests: Bayesian detector, MAP and ML detector, Neyman-Pearson detector, minimax detector
  • Detection of known signals in Gaussian noise: correlator, matched filter
  • Detection of Gaussian signals in Gaussian noise: estimator-correlator, canonical implementation
  • Composite hypothesis tests: Bayesian detector, uniformly most powerful detector, locally optimal detector, invariant detectors, generalized likelihood-ratio detector
  • Detection of deterministic signals with unknown parameters: subspace detector, CFAR detector

Teaching methods

Conventional lectures on the blackboard supported by electronic media.

Mode of examination

Oral

Additional information

The lecture is given every Monday at 1:15pm in Sem. 389 (CG0118).

First class: March 7, 2022 at 1pm

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Mon13:15 - 15:0007.03.2022 - 27.06.2022Sem 389 Vorlesung
Signal Detection - Single appointments
DayDateTimeLocationDescription
Mon07.03.202213:15 - 15:00Sem 389 Vorlesung
Mon14.03.202213:15 - 15:00Sem 389 Vorlesung
Mon21.03.202213:15 - 15:00Sem 389 Vorlesung
Mon28.03.202213:15 - 15:00Sem 389 Vorlesung
Mon04.04.202213:15 - 15:00Sem 389 Vorlesung
Mon25.04.202213:15 - 15:00Sem 389 Vorlesung
Mon02.05.202213:15 - 15:00Sem 389 Vorlesung
Mon09.05.202213:15 - 15:00Sem 389 Vorlesung
Mon16.05.202213:15 - 15:00Sem 389 Vorlesung
Mon23.05.202213:15 - 15:00Sem 389 Vorlesung
Mon30.05.202213:15 - 15:00Sem 389 Vorlesung
Mon13.06.202213:15 - 15:00Sem 389 Vorlesung
Mon20.06.202213:15 - 15:00Sem 389 Vorlesung
Mon27.06.202213:15 - 15:00Sem 389 Vorlesung

Examination modalities

oral exam

Course registration

Not necessary

Curricula

Literature

Lecture notes for this course are available.

Further References

  • Stephen M. Kay, "Fundamentals of Statistical Signal Processing: Detection Theory," Prentice-Hall: 1998.
  • H. Vincent Poor, "An Introduction to Signal Detection and Estimation," Springer: 1988.
  • Louis L. Scharf, "Statistical Signal Processing," Addison-Wesley: 1991.

Previous knowledge

probability, random variables, stochastic processes, linear algebra (on the level of the courses "Signal Processing 1" und "Signal Processing 2")

Language

English