Registration does not begin until 02.10.2026 at 08:00. It ends on 23.10.2026 at 23:59.

The course is structured in three parts:
- Kalman Filters: the linear case, the nonlinear case (extended and augmented Kalman filters), cascaded Kalman Filters, Dual Kalman Filters
- Recursive Least Squares Methods System Identification
- Introduction to Wavelets packets: Data Analysis and Reconciliation

The topics mentioned above are developed using Matlab/Simulik directly in the Computers Room together with the teacher.

The course intends to give the students the possibility to learn in an intuitive way some of the most important algorithms in the fields of System Identification, Data Analysis and Data Reconciliation. The central part of the course is represented by an intuitive introduction of Kalman Filters after introducing the Recursive Least Squares. The course will enable students to approach different identification problems, in an autonomous way, using different kinds of Kalman Filters acquiring also good command of Matlab/Simulink for different kinds of application problems. At the end of the course, a short introduction to Wavelet Packets will be offered in a way that the students can autonomously use wavelet packets to analyze signals and data to detect outliers and noise.

Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/evaluation/course-evaluation.html