MOOC - Sensor Fusion Expert
Sensor Fusion In-Depth
The last unit of this job role makes a profound and detailed description of sensor fusion and provides insights on important notions and procedures when performing sensor fusion techniques.
Filtering:
This learning element addresses the fundamental aspects of filtering, focusing on the Kalman Filter, Linear Kalman filter, Extended Kalman Filter, Unscented Kalman Filter and Particle Filter.
SFE.U4.E4.PC1
The student understands and demonstrates the nature and purpose of filtering.
SFE.U4.E4.PC2
The student is able to describe the essential properties of the Kalman filter.
SFE.U4.E4.PC3
The student understands, compares and distinguishes the different types of Kalman filters.
SFE.U4.E4.PC4
The student knows the different use cases of each type of Kalman filter.
SFE.U4.E4.PC5
The student is able to describe the essential properties of Particle filters.
SFE.U4.E4.PC6
The student is able to critically select a suitable filtering technique to a specific problem depending on the type of system dynamics and noise characteristics.
SFE.U4.E4.PC7
The student is able to develop Kalman as well as Particle filtering algorithms for solving sensor fusion problems.