I’m taking this course in Q3 during my first year at Twente. The aim is to learn how to design optimal estimators that can uncover the time-dependent states of a dynamic system. It includes state-of-the-art architectures
- Kalman Filter for linear and Gaussian systems
- Extended Kalman Filter for near-linear and near-Gaussian systems
- Particle Filter for non-linear and non-Gaussian systems
The accent of the course is on the utilization aspects, not on a mathematically rigorous treatment of the topic. One of the challenges addressed in the course is how to bring theoretical concepts to a practical solution.
Topics covered so far
Sadly, we use MATLAB.
References:
- van der Heijden et al: Classification, Parameter Estimation and State Estimation, 2004, J Wiley.