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Box-particle probability hypothesis density filtering

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Box-particle probability hypothesis density filtering. / Schikora, Marek; Gning, Amadou; Mihaylova, Lyudmila et al.
In: IEEE Transactions on Aerospace and Electronic Systems, Vol. 50, No. 3, 07.2014, p. 1660-1672.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Schikora, M, Gning, A, Mihaylova, L, Cremers, D & Koch, W 2014, 'Box-particle probability hypothesis density filtering', IEEE Transactions on Aerospace and Electronic Systems, vol. 50, no. 3, pp. 1660-1672. https://doi.org/10.1109/TAES.2014.120238

APA

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. https://doi.org/10.1109/TAES.2014.120238

Vancouver

Schikora M, Gning A, Mihaylova L, Cremers D, Koch W. Box-particle probability hypothesis density filtering. IEEE Transactions on Aerospace and Electronic Systems. 2014 Jul;50(3):1660-1672. doi: 10.1109/TAES.2014.120238

Author

Schikora, Marek ; Gning, Amadou ; Mihaylova, Lyudmila et al. / Box-particle probability hypothesis density filtering. In: IEEE Transactions on Aerospace and Electronic Systems. 2014 ; Vol. 50, No. 3. pp. 1660-1672.

Bibtex

@article{a427929f5f1d4e89b90865c6c9b8fc3b,
title = "Box-particle probability hypothesis density filtering",
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 overnetworks. 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 considerablyless 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. ",
keywords = "Multi-Target Tracking, Box-Particle Filters, Random Finite Sets, Probability hypothesis density filters, Interval Measurements",
author = "Marek Schikora and Amadou Gning and Lyudmila Mihaylova and Daniel Cremers and Wofgang Koch",
year = "2014",
month = jul,
doi = "10.1109/TAES.2014.120238",
language = "English",
volume = "50",
pages = "1660--1672",
journal = "IEEE Transactions on Aerospace and Electronic Systems",
issn = "0018-9251",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Box-particle probability hypothesis density filtering

AU - Schikora, Marek

AU - Gning, Amadou

AU - Mihaylova, Lyudmila

AU - Cremers, Daniel

AU - Koch, Wofgang

PY - 2014/7

Y1 - 2014/7

N2 - 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 overnetworks. 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 considerablyless 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.

AB - 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 overnetworks. 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 considerablyless 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.

KW - Multi-Target Tracking

KW - Box-Particle Filters

KW - Random Finite Sets

KW - Probability hypothesis density filters

KW - Interval Measurements

U2 - 10.1109/TAES.2014.120238

DO - 10.1109/TAES.2014.120238

M3 - Journal article

VL - 50

SP - 1660

EP - 1672

JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

SN - 0018-9251

IS - 3

ER -