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Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking

Baum, Marcus; Noack, Benjamin; Beutler, Frederik; Itte, Dominik; Hanebeck, Uwe D.

This paper is about tracking multiple targets with the so-called Symmetric Measurement Equation (SME) filter. The SME filter uses symmetric functions, e.g., symmetric polynomials, in order to remove the data association uncertainty from the measurement equation. By this means, the data association problem is converted to a nonlinear state estimation problem. In this work, an efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems corrupted with Gaussian noise is derived, and then applied to the polynomial system resulting from the SME filter. The performance of the new method is compared to an UKF implementation by means of typical multiple target tracking scenarios.

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Volltext §
DOI: 10.5445/IR/1000035110
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2011
Sprache Englisch
Identifikator ISBN: 978-1-4577-0267-9
KITopen-ID: 1000035110
Erschienen in Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011
Verlag IEEE, Piscataway
Seiten 8 S.
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