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dc.contributor.authorCaballero-Águila, Raquel
dc.contributor.authorHermoso Carazo, Aurora 
dc.contributor.authorLinares Pérez, Josefa 
dc.date.accessioned2021-03-22T07:19:19Z
dc.date.available2021-03-22T07:19:19Z
dc.date.issued2019-01
dc.identifier.citationCaballero-Águila, R., Hermoso-Carazo, A., Linares-Pérez, J.,Wang, Z. (2019). A new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noises. Information Fusion 45, 324–332.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/67363
dc.description.abstractThis paper is concerned with the distributed filtering problem for a class of discrete-time stochastic systems over a sensor network with a given topology. The system presents the following main features: (i) random parameter matrices in both the state and observation equations are considered; and (ii) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. The state estimation is performed in two stages. At the first stage, through an innovation approach, intermediate distributed least-squares linear filtering estimators are obtained at each sensor node by processing available output measurements not only from the sensor itself but also from its neighboring sensors according to the network topology. At the second stage, noting that at each sampling time not only the measurement but also an intermediate estimator is available at each sensor, attention is focused on the design of distributed filtering estimators as the least-squares matrix-weighted linear combination of the intermediate estimators within its neighborhood. The accuracy of both intermediate and distributed estimators, which is measured by the error covariance matrices, is examined by a numerical simulation example where a four-sensor network is considered. The example illustrates the applicability of the proposed results to a linear networked system with state-dependent multiplicative noise and different network-induced stochastic uncertainties in the measurements; more specifically, sensor gain degradation, missing measurements and multiplicative observation noises are considered as particular cases of the proposed observation model.es_ES
dc.description.sponsorshipThis research is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014- 52291-P, MTM2017-84199-P).es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectSensor Networkses_ES
dc.subjectDistributed filteringes_ES
dc.subjectRandom Parameter Matriceses_ES
dc.subjectCorrelated noiseses_ES
dc.titleA new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noiseses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.identifier.doihttps://doi.org/10.1016/j.inffus.2018.02.006
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España