A Novel Kullback-Leibler Divergence Minimization-Based Adaptive Student's t-Filter
- Submitting institution
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The University of Leicester
- Unit of assessment
- 12 - Engineering
- Output identifier
- 2405
- Type
- D - Journal article
- DOI
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10.1109/TSP.2019.2939079
- Title of journal
- IEEE Transactions on Signal Processing
- Article number
- -
- First page
- 5417
- Volume
- 67
- Issue
- 20
- ISSN
- 1053-587X
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- 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
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2
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Conventional adaptive signal processing algorithms are known to suffer from measurement outliers drawn from, for example, leptokurtic distributions. This seminal work led from the University of Leicester presents a fundamentally new pathway for the derivation of robust adaptive filters based on distribution rather than moment matching. It is attracting much international interest and is providing a platform for further advancements in the field together with new application domains such as tracking position and velocity of unmanned underwater vehicles through joint work with Harbin Engineering University.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -