ET4386 Estimation and Detection
Introduction
This course covers the basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, loclization, system identification, and elsewhere.Part I: Optimal estimation covers minimum variance unbiased (MVU) estimators, the Cramer-Rao bound (CRB), best linear unbiased estimators (BLUE), maximum likelihood estimation (MLE), recursive least squares (RLE), Bayesian estimation techniques, and the Wiener filter.
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 course complements EE4c03 Statistical digital signal processing and modeling, and gives a solid background for EE4715 Array Processing and EE4685 Machine learning, a Bayesian perspective.
Preliminary knowledge
To follow the course, you will need background knowledge in Random Signals and Linear Algebra.Books
Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory; S.M. Kay, Prentice Hall 1993; ISBN-13: 978-0133457117.
Fundamentals of Statistical Signal Processing, Volume II: Detection Theory; S.M. Kay, Prentice 1993; ISBN-13: 978-0135041352.
Projects
For details, see the Brightspace page of this course.Schedule
In 2024/2025, The lectures are scheduled on Mondays (1345-1545) and Thursdays (1545-1745). The complete schedule, slides and other instructions can be found on Brightspace.
Exam
In principle, the exam in the study year 2024/2025 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 practical problem. The available mini projects will be announced via the course website, after which students can sign in via Brightspace. The mini projects are encourage to be performed in groups of 2.