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.

• 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

Remote teaching in the form of a flipped classroom:
* lecture videos for all topics are available at TUPeerTube
* before class: students study the relevant videos and course materials
* in class (i.e., zoom meeting): discussion of the material, further examples, Q&A

All lectures are given on Mondays at 1:00pm via Zoom:

https://tuwien.zoom.us/j/96079517297?pwd=S2tjSnhUdWwyS0dLU2g1cU1paVJ4Zz09

Meeting ID: 960 7951 7297
Password: 15UYmLq5

First class: March 1, 2021 at 1:30pm

Recordings of the lectures are available on the following TUpeerTube channel:

https://tube1.it.tuwien.ac.at/video-channels/signaldetection

All other materials (slides, scans, etc.) are provided via TISS

oral exam

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.

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