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Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs

Baum, Marcus; Hanebeck, Uwe D.

This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM) that assumes varying measurement sources to lie on scaled versions of the shape boundaries. In this paper, a star-convex RHM is introduced for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHM is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.

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Volltext §
DOI: 10.5445/IR/1000035108
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2011
Sprache Englisch
Identifikator ISBN: 978-0-9824438-2-8
KITopen-ID: 1000035108
Erschienen in Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011
Verlag IEEE, Piscataway
Seiten 8 S.
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