A Box Regularized Particle Filter for state estimation with severely ambiguous and non-linear measurements
- Submitting institution
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Coventry University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 22901873
- Type
- D - Journal article
- DOI
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10.1016/j.automatica.2019.02.033
- Title of journal
- Automatica
- Article number
- -
- First page
- 102
- Volume
- 104
- Issue
- -
- ISSN
- 0005-1098
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Established the scientific foundations that enable particle filters to be computationally feasible components of the guidance/navigation/control framework for UAVs and other autonomous vehicles. The method allows for significantly more accurate estimate of state compared to state of the art and has low computational load for highly complex problems such as Terrain Aided Navigation. The work was conducted collaboratively with French Aerospace Lab ONERA and Paris Saclay under a cotutelle. Led to continued engagement with ONERA with a second cotutelle. The lead author (nicolas.merlinge@onera.fr) is applying the work and investigating its use with real devices.
- Author contribution statement
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- Non-English
- No
- English abstract
- -