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dc.contributor.author
Marelli, Damian Edgardo
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dc.contributor.author
Sui, Tianju
dc.contributor.author
Fu, Minyue
dc.date.available
2022-12-30T01:21:48Z
dc.date.issued
2021-08
dc.identifier.citation
Marelli, Damian Edgardo; Sui, Tianju; Fu, Minyue; Distributed Kalman estimation with decoupled local filters; Pergamon-Elsevier Science Ltd; Automatica; 130; 109724; 8-2021; 1-11
dc.identifier.issn
0005-1098
dc.identifier.uri
http://hdl.handle.net/11336/182875
dc.description.abstract
We study a distributed Kalman filtering problem in which a number of nodes cooperate without central coordination to estimate a common state based on local measurements and data received from neighbors. This is typically done by running a local filter at each node using information obtained through some procedure for fusing data across the network. A common problem with existing methods is that the outcome of local filters at each time step depends on the data fused at the previous step. We propose an alternative approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. The main feature of the new approach is that fusion errors introduced at a given time step do not carry over to subsequent steps. This offers advantages in many situations including when a global estimate is only needed at a rate slower than that of measurements or when there are network interruptions. If the global structural data can be fused correctly asymptotically, the stability of local filters is equivalent to that of the centralized Kalman filter. Otherwise, we provide conditions to guarantee stability and bound the resulting estimation error. Numerical experiments are given to show the advantage of our method over other existing alternatives.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ESTIMATION THEORY
dc.subject
KALMAN FILTERS
dc.subject
NETWORKED CONTROL SYSTEMS
dc.subject
SENSOR NETWORKS
dc.subject
STABILITY ANALYSIS
dc.subject
STATISTICAL ANALYSIS
dc.subject.classification
Control Automático y Robótica
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Distributed Kalman estimation with decoupled local filters
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-08-31T14:58:58Z
dc.journal.volume
130
dc.journal.number
109724
dc.journal.pagination
1-11
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Guangdong University Of Technology; China
dc.description.fil
Fil: Sui, Tianju. Dalian University Of Technology; China
dc.description.fil
Fil: Fu, Minyue. Universidad de Newcastle; Australia
dc.journal.title
Automatica
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.automatica.2021.109724
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0005109821002442
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