Clustering algorithms for localization
|Supervisor:||Horst Hellbrück , Marco Cimdins|
Clustering is used to find similarity structures in measurement data. The initial situation is a variety of points that are distributed in an area. The clustering algorithm is used to find the groups of those points.
The focus of this project is to examine different clustering algorithms for localization. Random samples, so-called particles, which represent a possible location of a person are used as the input data. When multiple persons are moving within a target area the particles will be distributed. The clustering algorithm is used to detect this and assign each particle a certain cluster.
- Training in cluster analysis
- Training in Python or MATLAB
- Documentation and evaluation of the results
- Knowledge of statistics
- Experience in MATLAB or Python