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dc.contributor.authorCaballero-Águila, Raquel
dc.contributor.authorHermoso Carazo, Aurora 
dc.contributor.authorLinares Pérez, Josefa 
dc.date.accessioned2021-03-22T07:17:06Z
dc.date.available2021-03-22T07:17:06Z
dc.date.issued2019-02
dc.identifier.citationCaballero-Águila, R., Hermoso-Carazo, A., Linares-Pérez, J. (2019). Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels. Digital Signal Processing 85, 77–85.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/67362
dc.description.abstractThe least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model.es_ES
dc.description.sponsorshipThis research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigaciónand Fondo Europeo de Desarrollo Regional FEDER (grant no. 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.subjectCentralized fusion estimationes_ES
dc.subjectRandom parameter matriceses_ES
dc.subjectUncertain observationses_ES
dc.subjectRandom delayses_ES
dc.subjectPacket dropoutses_ES
dc.titleCentralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.identifier.doihttps://doi.org/10.1016/j.dsp.2018.11.010
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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