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dc.contributor.authorGarcía-Ligero Ramírez, María Jesús 
dc.contributor.authorHermoso Carazo, Aurora 
dc.contributor.authorLinares Pérez, Josefa 
dc.date.accessioned2021-01-27T12:32:02Z
dc.date.available2021-01-27T12:32:02Z
dc.date.issued2020-11-04
dc.identifier.citationGarcía-Ligero, M. J., Hermoso-Carazo, A., & Linares-Pérez, J. (2020). Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays. Mathematics, 8(11), 1948. [doi:10.3390/math8111948]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/66077
dc.description.abstractThis paper investigates the distributed fusion estimation of a signal for a class of multi-sensor systems with random uncertainties both in the sensor outputs and during the transmission connections. The measured outputs are assumed to be affected by multiplicative noises, which degrade the signal, and delays may occur during transmission. These uncertainties are commonly described by means of independent Bernoulli random variables. In the present paper, the model is generalised in two directions: (i) at each sensor, the degradation in the measurements is modelled by sequences of random variables with arbitrary distribution over the interval [0, 1]; (ii) transmission delays are described using three-state homogeneous Markov chains (Markovian delays), thus modelling dependence at different sampling times. Assuming that the measurement noises are correlated and cross-correlated at both simultaneous and consecutive sampling times, and that the evolution of the signal process is unknown, we address the problem of signal estimation in terms of covariances, using the following distributed fusion method. First, the local filtering and fixed-point smoothing algorithms are obtained by an innovation approach. Then, the corresponding distributed fusion estimators are obtained as a matrix-weighted linear combination of the local ones, using the mean squared error as the criterion of optimality. Finally, the efficiency of the algorithms obtained, measured by estimation error covariance matrices, is shown by a numerical simulation example.es_ES
dc.description.sponsorshipMinisterio de Economía, Industria y Competitividades_ES
dc.description.sponsorshipEuropean Union (EU) MTM2017-84199-Pes_ES
dc.description.sponsorshipAgencia Estatal de Investigaciónes_ES
dc.language.isoenges_ES
dc.publisherMdpies_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectDistributed fusion estimationes_ES
dc.subjectSensor Networkses_ES
dc.subjectGain degradationes_ES
dc.subjectMarkovian delayses_ES
dc.subjectCorrelated noiseses_ES
dc.titleDistributed Fusion Estimation with Sensor Gain Degradation and Markovian Delayses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/math8111948
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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