General Information
Welcome to TSKS15 Detection and Estimation of Signals (HT 2026).
This course treats statistical signal processing, specifically parameter estimation and detection of signals. The purpose of the course is to provide a solid foundation in algorithms, models, methods and theory for the extraction of information from noisy signals. Applications are found for example within radar systems, communications systems, positioning systems and image analysis.
News
- Oct 09, 2026: I uploaded the slides from today's lecture to Lisam.
- Sep 24, 2026: I uploaded a new handout on the different "worldviews" (orthodox vs. Bayesian). It contains both a summary of the ideas that we discuss in the lectures and some extra material, which is beyond the scope of this course (but still very relevant and interesting).
- Sep 23, 2026: I uploaded Siying's solutions to the problems from the first three tutorials to Lisam.
- Sep 22, 2026: I updated the "Useful Formulas" sheet and uploaded the new version to Lisam.
- Sep 21, 2026: I updated the notebook with the FIR filter example from today's lecture. It now includes an additional probing signal and more explanations.
- Sep 09, 2026: I released a new interactive notebook illustrating the Cramer-Rao bound for the frequency estimation example that we discussed at the end of today's lecture.
- Sep 08, 2026: I released a new interactive notebook illustrating the concepts of Bayesian (multiple) hypothesis testing (prior, likelihood, and posterior).
- Aug 31, 2026: I uploaded the slides from today's lecture to Lisam. Do not forget to sign up for the labs on Lisam.
- Jul 28, 2026: The next TSKS15 exam will be on August 17, 2026. Do not forget to register if you want to participate.
Prerequisites
- Linear algebra
- Probability theory
- General mathematical maturity and understanding of electrical/systems engineering
- Programming skills (Python or Matlab)
As preparation for the course, all students should review the following concepts:
- Vectors, matrices, transpose, inverse, determinant, positive (semi-)definite forms, ...
- Vector-valued random variables, (multivariate) Gaussian distributions
- Mean and covariance, statistical independence
- Conditional and marginal probability, Bayes rule
Instructors
- Lectures: Karl-Ludwig Besser
- Tutorials: Siying Chen
- Labs: Siying Chen
Textbooks
- S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (Volume I), Prentice-Hall
- S. Kay, Fundamentals of Statistical Signal Processing: Detection Theory (Volume II), Prentice-Hall
Exam
There will be a written exam on October 28, 2026 (14-18).