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dc.contributor.authorCaballero-Águila, Raquel
dc.contributor.authorHermoso-Carazo, Aurora
dc.contributor.authorLinares-Pérez, Josefa
dc.description.abstractThis paper focuses on the least-squares linear fusion filter design for discrete-time stochastic signals from multisensor measurements perturbed not only by additive noise, but also by different uncertainties that can be comprehensively modeled by random parameter matrices. The additive noises from the different sensors are assumed to be cross-correlated at the same time step and correlated with the signal at the same and subsequent time steps. A covariancebased approach is used to derive easily implementable recursive filtering algorithms under the centralized, distributed and sequential fusion architectures. Although centralized and sequential estimators both have the same accuracy, the evaluation of their computational complexity reveals that the sequential filter can provide a significant reduction of computational cost over the centralized one. The accuracy of the proposed fusion filters is explored by a simulation example, where observation matrices with random parameters are used to describe different kinds of sensor uncertainties.es_ES
dc.description.sponsorshipThis research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER [grant number MTM2017- 84199-P].es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectCentralized fusion estimationes_ES
dc.subjectDistributed fusion estimationes_ES
dc.subjectSequential fusion estimationes_ES
dc.subjectCovariance informationes_ES
dc.subjectRandom measurement matriceses_ES
dc.titleCentralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signales_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