Box-particle probability hypothesis density filtering
- Submitting institution
-
The University of Hull
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 3402391
- Type
- D - Journal article
- DOI
-
10.1109/taes.2014.120238
- Title of journal
- IEEE Transactions on Aerospace and Electronic Systems
- Article number
- -
- First page
- 1660
- Volume
- 50
- Issue
- 3
- ISSN
- 0018-9251
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2014
- URL
-
https://ieeexplore.ieee.org/document/6965728
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This pioneering paper, part of a series of papers, presented a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach was able to track multiple targets and estimate the unknown number of targets and deal with stochastic, set-theoretic, and data association uncertainty. The implementation of random finite set based multi-target tracking filters (the so-called PHD filter, CPHD filter, Bernoulli and MeMBer filters) were all subsequently implemented following this paper. This has had a direct, positive, impact on multitarget tracking accuracy with considerably less computational costs.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -