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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.

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Metadaten
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):License LogoUrheberrechtlich geschützt