Box-particle probability hypothesis density filtering

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

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.

Citation

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

• 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
• DOI: https://doi.org/10.1109/taes.2014.120238
• Keywords: Atmospheric measurements; Particular measurements; Approximation methods; Noise measurement; Uncertainty; Target tracking
• Public URL: https://hull-repository.worktribe.com/output/1199841
• Publisher URL: https://ieeexplore.ieee.org/document/6965728