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Partial Likelihood for Unbiased Extended Object Tracking

Faion, Florian 1; Zea, A. 1; Baum, M. 1; Hanebeck, U. D. 1
1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)

Abstract:

An extended object gives rise to several measurements that originate from unknown measurement sources on the object. In this paper, we consider the tracking and parameter estimation of extended objects that are modeled as a curve in 2D such as a circle or an ellipse. A standard model for such extended objects is to assume that the unknown measurement sources are uniformly distributed on the curve. We argue that the uniform distribution may not be the best choice in scenarios where the true distribution of the measurements significantly differs from a uniform distribution. Based on results from curve fitting and errors-in-variables models, we develop a partial likelihood that ignores the distribution of measurement sources and can be shown to outperform the likelihood for a uniform distribution in these scenarios. If the true measurement sources are in fact uniformly distributed, our new likelihood results in a slightly slower convergence but has the same asymptotic behavior.


Scopus
Zitationen: 24
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2015
Sprache Englisch
Identifikator ISBN: 978-0-9824-4386-6
KITopen-ID: 1000051021
Erschienen in Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), 6-9 July 2015, Washington, DC, USA
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 1022-1029
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