An auxiliary particle filtering algorithm with inequality constraints
- Submitting institution
-
Loughborough University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 513
- Type
- D - Journal article
- DOI
-
10.1109/TAC.2016.2624698
- Title of journal
- IEEE Transactions on Automatic Control
- Article number
- -
- First page
- 4639
- Volume
- 62
- Issue
- 9
- ISSN
- 0018-9286
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2016
- URL
-
-
- 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
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has had a significant impact on the state of the art in the field of Bayesian filtering. This algorithm is significant because the constraint information is explicitly utilised in the filter design to construct better representative samples of the true posterior distribution, thus outperforming many existing methods. It can be used as a corner stone in target tracking applications for autonomous driving, where target vehicles are traveling within road boundary.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
