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DOI: 10.5445/IR/1000035108

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.

Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator ISBN: 978-0-9824438-2-8
URN: urn:nbn:de:swb:90-351087
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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