Partial diffusion Kalman filtering for distributed state estimation in multiagent networks
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 27 - 911217
- Type
- D - Journal article
- DOI
-
10.1109/tnnls.2019.2899052
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 3839
- Volume
- 30
- Issue
- 12
- ISSN
- 2162-2388
- Open access status
- Compliant
- Month of publication
- March
- 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
-
3
- Research group(s)
-
A - Computing and Informatics Research Centre
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- To tackle the communication failure for wireless sensor networks (WSNs) with limited power resources, in this novel fully distributed state estimation algorithm, every sensor shares only a subset of its intermediate estimates with its neighbours. This reduces the internode communications, is stable and convex, hence is extremely useful for energy saving in decentralised WSNs. Through collaboration with Ilam University, Iran, a new research direction in energy-efficient, and smart WSNs, highly usable by researchers in IoT cyberphysical systems (10.1049/iet-cps.2019.0028) and set- and game-theoretic estimation has been established. The achievements became a platform for three more published research directions within our group.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
