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Association-Free Direct Filtering of Multi-Target Random Finite Sets with Set Distance Measures

Hanebeck, Uwe D. 1; Baum, Marcus 1
1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)

Abstract:

We consider association-free tracking of multiple targets without identities. The uncertain multi-target state and the uncertain measurements cannot be described by a random vector as this would imply a certain order. Instead, they are described by an unordered random finite set (RFS). Particle-based random finite set densities are used for characterizing the RFS in a simple and natural way. For recursive Bayesian filtering, optimal multi-target state estimates are calculated by systematically minimizing an appropriate set distance measure while directly operating on the particles. Although methods for calculating point estimates of random finite set densities based on appropriate distance measures are available in literature, the proposed recursive filtering is a novel contribution.


Scopus
Zitationen: 8
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: 1000051028
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 1367-1374
Nachgewiesen in Scopus
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