A Novel Outlier-Robust Kalman Filtering Framework based on Statistical Similarity Measure
- Submitting institution
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The University of Leicester
- Unit of assessment
- 12 - Engineering
- Output identifier
- 2404
- Type
- D - Journal article
- DOI
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10.1109/tac.2020.3011443
- Title of journal
- IEEE Transactions on Automatic Control
- Article number
- .
- First page
- .
- Volume
- (Online First)
- Issue
- -
- ISSN
- 0018-9286
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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-
- 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
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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
- Robust adaptive filtering is crucial in underwater contexts as the Gaussian noise assumption often breaks down. The work is extremely significant as it represents an entirely novel framework for outlier-robust Kalman filtering led from the University of Leicester with one of the world's most highly cited researchers in the field at the University of Adelaide, Professor Peng Shi peng.shi@adelaide.edu.au. It is one of a series of outputs in the field of robust filtering for navigation applications generated by the team and the platform for a successful approx £500k bid for a collaborative 111 project in intelligent ship engineering.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -