Aim of the course

The goal of this course is to give a solid background in statistical estimation theory and some detection theory.This should provide the student with an excellent toolbox for model-based analysis of measured data.

The tools are useful in a multitude of application areas that involve data and mathematical models, such as Wireless Communications, Radar and Sonar Systems, Image Analysis, Control and Mechatronic Systems, Remote Sensing and Environmental Surveillance, Seismology, Astronomy and many others.

The focus is on the fundamental theory, which is exemplified using real-world examples.

Content

Mathematical Statistics Basics, Minimum Variance Unbiased Estimation and the Cramer-Rao Lower Bound, Best Linear Unbiased Estimators, Maximum Likelihood Estimation, Least Squares, Method of Moments and Instrumental Variables, Bayesian Estimation, Wiener and Kalman Filters.

Introduction to Detection Theory: Binary Hypothesis, Likelihood Ratio Tests, Information-Theoretic Criteria.

Application examples from Wireless Communications, Radar Systems and more.

The  course description can be found HERE