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Sequential Monte Carlo method for the iFilter

: Schikora, M.; Koch, W.; Streit, R.L.; Cremers, D.

Dunham, Darin ; Institute of Electrical and Electronics Engineers -IEEE-; International Society of Information Fusion -ISIF-:
14th International Conference on Information Fusion 2011. Proceedings : Chicago, Illinois, USA, 5 - 8 July 2011
Piscataway, NJ: IEEE, 2011
ISBN: 978-1-4577-0267-9
ISBN: 978-0-9824438-2-8
8 pp.
International Conference on Information Fusion (FUSION) <14, 2011, Chicago/Ill.>
Conference Paper
Fraunhofer FKIE

Poisson point processes (PPP's) are very useful theoretical models for diverse applications. One of those is multitarget tracking of an unknown number of targets, leading to the intensity filter (iFilter) as a generalization of the probability hypothesis density (PHD) filter. This article develops a sequential Monte Carlo (SMC) implementation of the iFilter. In theory it was shown that the iFilter can estimate a clutter model from the measurements and thus does not need it as a-priori knowledge, like the PHD filter does. Our studies show that this property holds not only in simulations but also in real world applications. In addition it can be shown, that the performance of the PHD filter decreases substantially if the a-priori knowledge of the clutter intensity is chosen incorrectly.