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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.


Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2009
Sprache Englisch
Identifikator ISBN: 978-0-9824-4380-4
URN: 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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