Refine
Year of publication
- 2019 (3) (remove)
Document Type
- Conference Proceeding (2)
- Master's Thesis (1)
Language
- English (3)
Keywords
- Extended object tracking (2)
- Gaussian mixture (2)
- Laser sensor (2)
- Monte Carlo methods (1)
- Object tracking (1)
- PHD filter (2)
- Partitioning algorithms (1)
- Radar tracking (1)
- Sampling methods (1)
- Spline extension model (2)
Institute
This paper presents the integration of a spline based extension model into a probability hypothesis density (PHD) filter for extended targets. Using this filter the position and extension of each object as well as the number of present objects can jointly be estimated. Therefore, the spline extension model and the PHD filter are addressed and merged in a Gaussian mixture (GM) implementation. Simulation results using artificial laser measurements are used to evaluate the performance of the presented filter. Finally, the results are illustrated and discussed.