Box-particle intensity filter

Schikora, Marek and Gning, Amadou and Mihaylova, Lyudmila and Cremers, Daniel and Koch, Wofgang and Streit, Roy (2012) Box-particle intensity filter. In: Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. UNSPECIFIED, GBR.

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Abstract

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. Furthermore, 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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa75
Subjects:
ID Code:
54409
Deposited By:
Deposited On:
22 May 2012 09:08
Refereed?:
Yes
Published?:
Published
Last Modified:
19 Sep 2020 06:41