Tracking of Spline Modeled Extended Targets Using a Gaussian Mixture PHD Filter
- 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.
| Author: | Tim BaurORCiD, Julian Böhler, Stefan WirtensohnORCiD, Johannes ReuterORCiD |
|---|---|
| DOI: | https://doi.org/10.23919/FUSION43075.2019.9011298 |
| ISBN: | 978-0-9964527-8-6 |
| ISBN: | 978-1-7281-1840-6 |
| Parent Title (English): | 22th International Conference on Information Fusion (FUSION), 02-05 July 2019, Ottawa, ON, Canada |
| Publisher: | IEEE |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2019 |
| Release Date: | 2023/07/28 |
| Tag: | Extended object tracking; PHD filter; Laser sensor; Gaussian mixture; Spline extension model |
| Page Number: | 8 Seiten |
| Note: | Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich |
| Institutes: | Institut für Systemdynamik - ISD |
| Open Access?: | Nein |
| Licence (German): |  Urheberrechtlich geschützt |
