@article { , 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 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.}, doi = {10.1109/taes.2014.120238}, issn = {0018-9251}, issue = {3}, journal = {IEEE Transactions on Aerospace and Electronic Systems}, note = {Authors not affiliated to the University of Hull at the time of publication.}, pages = {1660-1672}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers}, url = {https://hull-repository.worktribe.com/output/1199841}, volume = {50}, keyword = {Specialist Research - Other, Atmospheric measurements, Particular measurements, Approximation methods, Noise measurement, Uncertainty, Target tracking}, year = {2014}, author = {Schikora, Marek and Gning, Amadou and Mihaylova, Lyudmila and Cremers, Daniel and Koch, Wolfgang} }