PhD Courses in Denmark

Welcome to Estimation and Filtering in Stochastic Dynamic Systems

Description: 
In applications within modeling, estimation, control and detection state space models are used. Normally the whole state can not be measured but only some function of the state is measured and perhaps also with substantial measurement noise. In at least the above applications it is necessary to estimate the whole state from the measurement. Exactly this is the topic for the course. For linear discrete time stochastic systems there exist the Kalman filter solution which to some extend is included in the master curriculum. The topic for this course is the advanced methods for nonlinear and continuous time systems and also to include parameter estimation. 
The purpose of this PhD course is to give the participant a comprehensive knowledge on both basic and more advanced aspects of state and parameter estimation. The goal is to enable the students with knowledge and tools for stochastic modeling of physical systems. The participant should be able to apply software for state and parameter estimation for the model structures.
The software used are programs from MATLAB. Topics include among others Kalman filters (KF), Extended KF, Unscented KF, continuous discrete filtering, stochastic differential equation (SDE), parameter estimation by extending the state or maximum likelihood (ML), particle filter (PF).
The course is evaluated as passed/not passed. In order to pass the student must be actively participating and deliver written solutions to the exercises.