• knowledge about fundamental concepts and methods of signal detection
• ability to formulate and solve practical detection problems
• improvement of technical english

• 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

Place: SEM 389 (room no. CG0118), Institute of Telecommunications

Date: Monday, 1:30-3:00 pm; first class: Monday, March 4, 2019

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")