Robust Distributed Diffusion Recursive Least Squares Algorithms with Side Information for Adaptive Networks
- Submitting institution
-
University of York
- Unit of assessment
- 12 - Engineering
- Output identifier
- 66224435
- Type
- D - Journal article
- DOI
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10.1109/TSP.2019.2893846
- Title of journal
- IEEE Transactions on Signal Processing
- Article number
- -
- First page
- 1566
- Volume
- 67
- Issue
- 6
- ISSN
- 1053-587X
- Open access status
- Compliant
- Month of publication
- January
- 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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4
- Research group(s)
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A - Communication Technologies
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The new strategy for distributed estimation using adaptive networks proposed here is robust in non-Gaussian environments, such as environments with impulsive noise, typical in practice. The DCD algorithm previously developed by Zakharov and incorporated here gives a practical strategy with lower complexity than previous solutions. This paper is a result of long-term collaboration with researchers from universities in Brazil (CETUC, PUC-Rio, Rio de Janeiro) and China (Southwest Jiaotong University in Chengdu). The work has impacted approaches in other areas including the total least square problem (doi: 10.1016/j.sigpro.2020.107954).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -