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DC Field | Value | Language |
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dc.contributor.author | Ding, D | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Ho, DWC | - |
dc.contributor.author | Wei, G | - |
dc.date.accessioned | 2017-03-30T11:49:47Z | - |
dc.date.available | 2017-04-01 | - |
dc.date.available | 2017-03-30T11:49:47Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Ding, D., Wang, Z., Ho, D.W.C. and Wei, G. (2017) 'Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks', Automatica, 78, pp. 231-240. doi: 10.1016/j.automatica.2016.12.026. | en_US |
dc.identifier.issn | 0005-1098 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/14341 | - |
dc.description.abstract | This paper is concerned with the distributed recursive filtering problem for a class of discrete time-delayed stochastic systems subject to both uniform quantization and deception attack effects on the measurement outputs. The target plant is disturbed by the multiplicative as well as additive white noises. A novel distributed filter is designed where the available innovations are from not only the individual sensor but also its neighboring ones according to the given topology. Attention is focused on the design of a distributed recursive filter such that, in the simultaneous presence of time-delays, deception attacks and uniform quantization effects, an upper bound for the filtering error covariance is guaranteed and subsequently minimized by properly designing the filter parameters via a gradient-based method at each sampling instant. Furthermore, by utilizing the mathematical induction, a sufficient condition is established to ensure the asymptotic boundedness of the sequence of the error covariance. Finally, a simulation example is utilized to illustrate the usefulness of the proposed design scheme of distributed filters. | en_US |
dc.description.sponsorship | Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61573246 and 61374039, the Research Grants Council of Hong Kong Special Administrative Region under grant GRF 11300415 and a grant from CityU 7004672, the Shanghai Rising-Star Program of China under Grant 16QA1403000, the Program for Capability Construction of Shanghai Provincial Universities under Grant 15550502500, and the Alexander von Humboldt Foundation of Germany. | - |
dc.format.extent | 231 - 240 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Sensor networks | en_US |
dc.subject | distributed filtering | en_US |
dc.subject | recursive filtering | en_US |
dc.subject | deception attacks | en_US |
dc.subject | uniform quantization | en_US |
dc.title | Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.automatica.2016.12.026 | - |
dc.relation.isPartOf | Automatica | - |
pubs.publication-status | Published | - |
pubs.volume | 78 | - |
dc.identifier.eissn | 1873-2836 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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