Box-particle probability hypothesis density filtering

Schikora M, Gning A, Mihaylova L, Cremers D, Koch W (2014)

Publication Type: Journal article

Publication year: 2014

Journal

IEEE Transactions on Aerospace and Electronic Systems Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 50

Pages Range: 1660-1672

Article Number: 6965728

Journal Issue: 3

DOI: 10.1109/TAES.2014.120238

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 (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.

Involved external institutions

Fraunhofer-Institut für Kommunikation, Informationsverarbeitung und Ergonomie (FKIE) / Fraunhofer Institute for Communication, Information Processing and Ergonomics DE Germany (DE) University College London (UCL) GB United Kingdom (GB) University of Sheffield GB United Kingdom (GB) Technische Universität München (TUM) DE Germany (DE)

How to cite

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

MLA:

Schikora, Marek, et al. "Box-particle probability hypothesis density filtering." IEEE Transactions on Aerospace and Electronic Systems 50.3 (2014): 1660-1672.

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