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Extension of the Sliced Gaussian Mixture Filter with Application to Cooperative Passive Target Tracking

Hörst, Julian; Sawo, Felix; Klumpp, Vesa; Hanebeck, Uwe D.; Fränken, Dietrich

Abstract:
This paper copes with the problem of nonlinear Bayesian state estimation. A nonlinear filter, the Sliced Gaussian Mixture Filter (SGMF), employs linear substructures in the nonlinear measurement and prediction model in order to simplify the estimation process. Here, a special density representation, the sliced Gaussian mixture density, is used to derive an exact solution of the Chapman-Kolmogorov equation. The sliced Gaussian mixture density is obtained by a systematic and deterministic approximation of a continuous density minimizing a certain distance measure. In contrast to previous work, improvements of the SGMF presented here include an extended system model and the processing of multi-dimensional nonlinear subspaces. As an application for the SGMF, cooperative passive target tracking, where sensors take angular measurements from a target, is considered in this paper. Finally, the performance of the proposed estimator is compared to the marginalized particle filter (MPF) in simulations.

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Volltext §
DOI: 10.5445/IR/1000034956
Coverbild
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2009
Sprache Englisch
Identifikator ISBN: 978-0-9824-4380-4
urn:nbn:de:swb:90-349560
KITopen-ID: 1000034956
Erschienen in Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, 6-9 July 2009
Verlag IEEE, Piscataway (NJ)
Seiten 587-594
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