The marginalized auxiliary particle filter

Carsten Fritsche, Thomas B. Schon, Anja Klein
2009 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)  
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 lter. The main contribution in the present work is to show how an ecient lter can be derived by exploiting this structure within the auxiliary particle lter. Based on a multisensor aircraft tracking example, the superior performance of the proposed
more » ... e of the proposed lter over conventional particle ltering approaches is demonstrated. Abstract-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 multisensor aircraft tracking example, the superior performance of the proposed filter over conventional particle filtering approaches is demonstrated.
doi:10.1109/camsap.2009.5413276 fatcat:ka6ilpeqwfaa5jjzxjuersiive