This is the latest version of this eprint.
Schikora, M., Gning, A., Mihaylova, L. et al. (2 more authors) (2014) Box-Particle Probability Hypothesis Density Filtering. IEEE Transactions on Aerospace and Electronic Systems , 50 (3). 1660 - 1672 . ISSN 0018-9251
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.
Metadata
Authors/Creators: |
|
---|---|
Copyright, Publisher and Additional Information: | © 2014 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Nov 2015 17:43 |
Last Modified: | 12 Apr 2016 14:17 |
Published Version: | https://dx.doi.org/10.1109/TAES.2014.120238 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | https://doi.org/10.1109/TAES.2014.120238 |
Related URLs: |
Available Versions of this Item
-
Box-Particle Probability Hypothesis Density Filtering. (deposited 29 Jan 2015 13:45)
- Box-Particle Probability Hypothesis Density Filtering. (deposited 19 Nov 2015 17:43) [Currently Displayed]