General Information
Welcome to TSKS15 Detection and Estimation of Signals (HT 2025).
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.
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: Jianan Bai
- Labs: Jianan Bai
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.