Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12317
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dc.contributor.authorHu, J-
dc.contributor.authorWang, Z-
dc.contributor.authorChen, D-
dc.contributor.authorAlsaadi, FE-
dc.date.accessioned2016-03-10T13:06:51Z-
dc.date.available2016-09-01-
dc.date.available2016-03-10T13:06:51Z-
dc.date.issued2016-
dc.identifier.citationInformation Fusion, 31: pp. 65 - 75, (2016)en_US
dc.identifier.issn1566-2535-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1566253516000087-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12317-
dc.description.abstractIn this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multi-objective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted.en_US
dc.format.extent65 - 75-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectEstimationen_US
dc.subjectFilteringen_US
dc.subjectMulti-sensor data fusionen_US
dc.subjectNetworked systemsen_US
dc.subjectNetwork-induced phenomenaen_US
dc.titleEstimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospectsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.inffus.2016.01.001-
dc.relation.isPartOfInformation Fusion-
pubs.publication-statusAccepted-
pubs.volume31-
Appears in Collections:Dept of Computer Science Research Papers

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