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Partial Likelihood for Unbiased Extended Object Tracking

Faion, Florian; Zea, A.; Baum, M.; Hanebeck, U.D.

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.

Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Jahr 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 IEEE, Piscataway (NJ)
Seiten 1022-1029
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