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URN: urn:nbn:de:swb:90-351069
Originalveröffentlichung
DOI: 10.1109/CDC.2011.6161522

Mixture Random Hypersurface Models for tracking multiple extended objects

Baum, Marcus; Noack, Benjamin; Hanebeck, Uwe D.

Abstract:
This paper presents a novel method for tracking multiple extended objects. The shape of a single extended object is modeled with a recently developed approach called Random Hypersurface Model (RHM) that assumes a varying number of measurement sources to lie on scaled versions of the shape boundaries. This approach is extended by introducing a so-called Mixture Random Hypersurface Model (Mixture RHM), which allows for modeling multiple extended targets. Based on this model, a Gaussian-assumed Bayesian tracking method that provides the means to track and estimate shapes of multiple extended targets is derived. Simulations demonstrate the performance of the new approach.


Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator ISBN: 978-1-61284-800-6

KITopen-ID: 1000035106
Erschienen in Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, December 12-15, 2011
Seiten 3166-3171
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