Box-particle probability hypothesis density filtering

• Published in: IEEE transactions on aerospace and electronic systems Vol. 50; no. 3; pp. 1660 - 1672
• Main Authors: Schikora, Marek, Gning, Amadou, Mihaylova, Lyudmila, Cremers, Daniel, Koch, Wolfgang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation methods; Atmospheric measurements; Noise measurement; Particle measurements; Target tracking; Uncertainty
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2014.120238

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.