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The Kernel-SME filter for multiple target tracking

Baum, M.; Hanebeck, U. D.

We present a novel method called Kernel-SME filter for tracking multiple targets when the association of the measurements to the targets is unknown. The method is a further development of the Symmetric Measurement Equation (SME) filter, which removes the data association uncertainty of the original measurement equation with the help of a symmetric transformation. The underlying idea of the Kernel-SME filter is to construct a symmetric transformation by means of mapping the measurements to a Gaussian mixture. This transformation is scalable to a large number of targets and allows for deriving a Gaussian state estimator that has a cubic time complexity in the number of targets.

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Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 24.12.2012
Sprache Englisch
Identifikator KITopen-ID: 1000120264
Umfang 9 S.
Nachgewiesen in arXiv
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