Estimation and Filtering in Stochastic Dynamic Systems

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.

Prerequisites: Knowledge on methods for describing dynamical systems in discrete time, and on statistics and stochastic processes at master level.

Learning objectives: 
Formulate discrete-time stochastic state-space models for physical systems, including appropriate process and measurement noise models.
Implement and tune linear-Gaussian filters.
Implement and tune nonlinear filters, selecting linearization/sigma-point schemes, and assess filter consistency, stability, and numerical robustness.
Deploy particle filters for nonlinear, non-Gaussian models, design proposal/resampling strategies, and quantify state uncertainty.
Estimate unknown parameters and inputs of linear and non-linear systems.
Diagnose observability and identifiability of states and parameters.
Integrate estimation with fault diagnosis, constructing residuals/innovation-based detectors and evaluate detection performance under noise and modeling errors.
Build reproducible MATLAB workflows to implement filters/estimators, run benchmarks on real or synthetic data, and communicate results in clear, technical reports.

Organizer: Torben Knudsen

Lecturers: Szymon Gres, Mohamad Al Ahdab and Torben Knudsen

ECTS: 3

Time: 22, 23, 24, 25 and 26 June 2026

Place: Aalborg University

City: Aalborg

Maximal number of participants: 20

Deadline: 1 June 2026

Important information concerning PhD courses: 

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.