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

Conventional lectures on the blackboard supported by electronic media.

The lecture is given every Monday at 1:00pm in Sem. 389 (CG0118).

First class: March 6, 2023 at 1pm

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")