Kies de Nederlandse taal
Course module: 202200106
Optimal Estimation in Dynamic Systems
Course info
Course module202200106
Credits (ECTS)5
Course typeCourse
Language of instructionEnglish
Contact persondr. F.J. Siepel
Contactperson for the course
dr. F.J. Siepel
dr. F.J. Siepel
Academic year2022
Starting block
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
Design of optimal state estimators
The course addresses the following problem: How to estimate the dynamic quantities in a physical process given the data from a sensory system? Although the applications are wide (ranging from production processes, water management, orbit determination, telecommunication and so on), the course will concentrate on robotic applications: navigation and tracking. Especially, the SLAM problem will be addressed. SLAM = simultaneous localisation and mapping, e.g. a mobile robot that has to navigate within an unseen environment. The course will familiarise the student with methods for the estimation of state variables in dynamic systems. The course starts with an introduction of the topic 'parameter estimation' which is the fundament for state estimation. After that, the estimation paradigm will be embedded in a dynamic framework. For linear-Gaussian systems this leads to the well-known Kalman filter which is an online estimation method. An extension of the Kalman filter makes it applicable to offline estimation, and to prediction. For nonlinear dynamic systems, the so-called 'extended Kalman filter' is a suboptimal solution which only works well if the nonlinearities are not severe and the disturbances are near Gaussian. Another estimation method is the 'particle filter'. This method is generally applicable, and is optimal, but it is computationally more intensive. An important aspect of the course is bringing a theoretical concept to a practical solution. Students that attend this course will design an estimator for a given navigation process. Various estimation methods (e.g. Kalman, extended Kalman, particle filtering) will be tested and evaluated with a tracking problem and SLAM problem. Matlab is used as a development platform.

Estimation, Kalman filter, extended Kalman filter, Particle filter, prediction, SLAM.
Participating study
Master Electrical Engineering
Participating study
Master Mechanical Engineering
Participating study
Master Systems and Control
Participating study
Master Biomedical Engineering
Participating study
Master Robotics
Required materials
F. van der Heijden et al: Classification, Parameter Estimation and State Estimation - An Engineering Approach using MatLab, J. Wiley & Sons, 2004, or 2nd edition, 2017.
Recommended materials
Instructional modes


Self study without assistance

Oral exam

Kies de Nederlandse taal