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Box-particle intensity filter

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


Institution of Engineering and Technology -IET-:
9th IET Data Fusion & Target Tracking Conference, DF&TT 2012. CD-ROM : Algorithms & Applications, 16.-17. May 2012, London, UK
London: IET, 2012
ISBN: 978-1-84919-624-6
ISBN: 1-84919-624-9
Data Fusion & Target Tracking Conference (DF&TT) <9, 2012, London>
Conference Paper
Fraunhofer FKIE ()

This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Further more, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.