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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Boxparticle PHD filter for multitarget tracking
 Institute of Electrical and Electronics Engineers IEEE: Fusion 2012, 15th International Conference on Information Fusion : 09.15. July 2012, Singapore New York, NY: IEEE, 2012 ISBN: 9781467304177 (Print) ISBN: 9780982443842 (Online) ISBN: 9780982443859 pp.106113 
 International Conference on Information Fusion (FUSION) <15, 2012, Singapore> 

 English 
 Conference Paper 
 Fraunhofer FKIE 
Abstract
This paper develops a novel approach for multitarget tracking, called boxparticle probability hypothesis density filter (boxPHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable to deal with three sources of uncertainty: stochastic, settheoretic and data association uncertainty. The boxPHD filter reduces the number of particles significantly, which improves the runtime considerably. The small particle number makes this approach attractive for distributed computing. A boxparticle is a random sample that occupies a small and controllable rectangular region of nonzero volume. Manipulation of boxes utilizes methods from the field of interval analysis. The theoretical derivation of the boxPHD filter is presented followed by a comparative analysis with a standard sequential Monte Carlo (SMC) version of the PHD filter. To measure the performance objectively three measures are used: inclusion, volume and the optimum subpattern assignment metric. Our studies suggest that the boxPHD filter reaches similar accuracy results, like a SMCPHD filter but with much considerably less computational costs. Furthermore, we can show that in the presence of strongly biased measurement the boxPHD filter even outperforms the classical SMCPHD filter.