The marginalized auxiliary particle filter

In this paper we are concerned with nonlinear systems subject to a conditionally linear, Gaussian sub-structure. This structure is often exploited in high-dimensional state estimation problems using the marginalized (aka Rao-Blackwellized) particle filter. The main contribution in the present work i...

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Bibliographic Details
Published in2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing pp. 289 - 292
Main Authors Fritsche, C., Schon, T.B., Klein, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
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ISBN1424451795
9781424451791
9781424451807
1424451809
DOI10.1109/CAMSAP.2009.5413276

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Summary:In this paper we are concerned with nonlinear systems subject to a conditionally linear, Gaussian sub-structure. This structure is often exploited in high-dimensional state estimation problems using the marginalized (aka Rao-Blackwellized) particle filter. The main contribution in the present work is to show how an efficient filter can be derived by exploiting this structure within the auxiliary particle filter. Based on a multi-sensor aircraft tracking example, the superior performance of the proposed filter over conventional particle filtering approaches is demonstrated.
ISBN:1424451795
9781424451791
9781424451807
1424451809
DOI:10.1109/CAMSAP.2009.5413276