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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. A Log homotopy based Particle Flow Solution for Mixture of Gaussian Prior Densities
 Institute of Electrical and Electronics Engineers IEEE: IEEE International Conference on Multisensor Fusion and lntegration for Intelligent Systems, MFI 2016 : Sept 1921, 2016, BadenBaden, Germany Piscataway, NJ: IEEE, 2016 ISBN: 9781467397087 ISBN: 9781467397094 S.546551 
 International Conference on Multisensor Fusion and lntegration for Intelligent Systems (MFI) <2016, BadenBaden> 
 European Commission EC FP7PEOPLE; 607400; TRAX Training network on tRAcking in compleX sensor systems 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer FKIE () 
Abstract
Particle flow filters, also known as DaumHuang filters (DHF), provide an alternative method for the state estimation of nonlinear / nonGaussian systems, in a Bayesian context. These filters incorporate the measurements in several steps, which is manifested in the form of the gradual update of particles states. Updates are performed by solving an ordinary differential equation, also called the flow equation. Several such equations have been derived, each based on distinct assumptions. Amongst others, the so called Exact flow has been more commonly used. It relies on approximating the prior density by a single multivariate Gaussian. In this paper we generalize this, and consider prior represented as a sum of Gaussian densities. We then derive particle flow equations and provide an implementation framework. We numerically show that the DHF based on our new flow outperforms the Exact flow based DHF, and achieves performance comparable to the bootstrap particle filter.