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Mixture Random Hypersurface Models for tracking multiple extended objects

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

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.

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Volltext §
DOI: 10.5445/IR/1000035106
DOI: 10.1109/CDC.2011.6161522
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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