Box-particle probability hypothesis density filtering

Schikora, Marek and Gning, Amadou and Mihaylova, Lyudmila and Cremers, Daniel and Koch, Wofgang (2014) Box-particle probability hypothesis density filtering. IEEE Transactions on Aerospace and Electronic Systems, 50 (3). pp. 1660-1672. ISSN 0018-9251

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Abstract

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets 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-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small number of box particles makes this approach attractive for distributed inference, especially when particles have to be shared over networks. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes methods from the field of interval analysis. The theoretical derivation of the box-PHD 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 box-PHD filter reaches similar accuracy results, like a SMC-PHD filter but with considerably less computational costs. Furthermore, we can show that in the presence of strongly biased measurement the box-PHD filter even outperforms the classical SMC-PHD filter.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Aerospace and Electronic Systems
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2202
Subjects:
ID Code:
66038
Deposited By:
Deposited On:
12 Aug 2013 10:58
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Dec 2020 02:08