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Box-particle probability hypothesis density filtering

Schikora, Marek; Gning, Amadou; Mihaylova, Lyudmila; Cremers, Daniel; Koch, Wolfgang


Marek Schikora

Amadou Gning

Lyudmila Mihaylova

Daniel Cremers

Wolfgang Koch


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 (OSPA) metric. Our studies suggest that the box-PHD filter reaches similar accuracy results, like an 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.


Schikora, M., Gning, A., Mihaylova, L., Cremers, D., & Koch, W. (2014). Box-particle probability hypothesis density filtering. IEEE Transactions on Aerospace and Electronic Systems, 50(3), 1660-1672.

Journal Article Type Article
Acceptance Date Apr 23, 2012
Online Publication Date Sep 20, 2013
Publication Date 2014-07
Deposit Date Jan 2, 2019
Journal IEEE Transactions on Aerospace and Electronic Systems
Print ISSN 0018-9251
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 50
Issue 3
Pages 1660-1672
Keywords Atmospheric measurements; Particular measurements; Approximation methods; Noise measurement; Uncertainty; Target tracking
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