Estimation and Detection

This course covers the basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, radar, biomedical, telecommunication, system identification, and elsewhere.

Part I: Optimal estimation covers minimum variance unbiased estimators, the Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, least squares, Bayesian estimation techniques including Wiener and Kalman filters.

Part II: Detection theory covers simple and multiple hypothesis testing, the Neyman-Pearson Theorem, Bayes Risk, and testing with unknown signal and noise parameters.

The exam for et4386 Estimation and Detection Theory will be a written exam. The exam is closed book, but, students are allowed to bring a double sided self HANDWRITTEN A4 formula sheet. As part of the course, there is a compulsary mini project, which helps you to get experienced with the theory and to apply this to a practicle problem. The avaialble mini projects will be announced via the course website, after which students can sign in. The mini projects can be done in pairs.

The final report on the project needs to be submitted in pdf before the date announced on the website.

The project report will be graded and counts for 20 % of the final grade. Access to the final exam will only be granted if the project is handed in.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).