Mostrar el registro sencillo del ítem

dc.contributor.advisorLinares-Pérez, Josefaes_ES
dc.contributor.advisorCaballero-Águila, R.es_ES
dc.contributor.authorGarcía Garrido, Irene es_ES
dc.contributor.otherUniversidad de Granada. Departamento de Estadística e Investigación Operativaes_ES
dc.date.accessioned2016-09-19T12:32:55Z
dc.date.available2016-09-19T12:32:55Z
dc.date.issued2016
dc.date.submitted2015-09-18
dc.identifier.citationGarcía Garrido, I. New advances in the estimation problem in systems with random failures. Granada: Universidad de Granada, 2016. [http://hdl.handle.net/10481/42603]es_ES
dc.identifier.isbn9788491255314
dc.identifier.urihttp://hdl.handle.net/10481/42603
dc.description.abstractThe aim of this PhD thesis is to address least-squares estimation problems in discrete-time linear systems from noisy measurements derived from multiple sensors, affected by random parameters which model different situations of failure in the mechanism or the transmission of the measurements. According to the kind of systems considered, the main contributions of this PhD thesis are summarized below: Sensor network systems with uncertain observations. These systems describe situations in which the mechanism of measurements may be randomly interrupted, in the sense that, at each instant of time, there is a positive probability that the corresponding observation is only noise, i.e., the observations may not contain information about the state. This kind of uncertainty is modeled by including in the observation equation not only an additive noise, but also a multiplicative noise component described by a sequence of Bernoulli random variables whose values, one or zero, indicate the presence or absence of the state in the corresponding measurement. In cases in which the Bernoulli variables are assumed to be correlated at instants that differ by m units of time, on the one hand, centralized and distributed fusion linear estimators are designed (Chapter 1) and, on the other, in order to improve the linear estimators, quadratic estimators are obtained using the centralized fusion method (Chapter 2). Sensor network systems with failures in the measurements, in which the observations from the different sensors may contain only partial information about the state. This kind of failure is more general than the previous one and it is described by a sequence of independent random variables with discrete probability distribution over the interval [0; 1]. For this class of systems, under the assumption that the system additive noises are autocorrelated and also cross-correlated, recursive linear filtering algorithms are derived using the centralized and distributed fusion methods (Chapter 3). Sensor network systems with random parameter matrices. This kind of systems constitute a more general framework than the previous ones since the state and/or the observation equations may be affected by random parameter matrices, thus covering numerous real situations with random failures in the measurements. First, we consider independent random state transition matrices, and one-step correlated and cross-correlated random parameter matrices in the observation equation; it is also assumed that the system noises are autocorrelated and cross-correlated. Using the centralized fusion method, a recursive linear filtering algorithm is obtained and the results are applied to multi-sensor systems with failures in the measurements described by random variables with discrete distribution over the interval [0; 1], and to multi-sensor systems with randomly delayed observations (Chapter 4). Second, the linear estimation problem in systems with independent random parameter matrices and correlated noises is addressed, using the distributed fusion method (Chapter 5). Finally, centralized quadratic estimators are obtained in systems with independent random parameter matrices and noises, and they are applied to systems with random failures in the measurements, described by different sequences of random variables with discrete probability distribution over the interval [0; 1] (Chapter 6).en_EN
dc.description.sponsorshipTesis Univ. Granada. Programa Oficial de Doctorado en Matemáticas y Estadísticaes_ES
dc.description.sponsorshipEsta tesis doctoral ha sido financiada por la beca del programa de Formación de Profesorado Universitario (FPU) del Ministerio de Educación, Cultura y Deporte, en su resolución del 20 de diciembre de 2011, con código de referencia AP2010-1553, así como por los proyectos No. MTM2011-24718 del Ministerio de Ciencia e Innovación, No. P07-FQM-02701 de la Junta de Andalucía y No. MTM2014-52291-P del Ministerio de Economía y Competitividad.es_ES
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherUniversidad de Granadaes_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 License
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectTeoría de la estimaciónes_ES
dc.subjectMatemáticas es_ES
dc.subjectEstadística es_ES
dc.subjectSistemas línealeses_ES
dc.subjectCongruencias y residuoses_ES
dc.subjectMatrices aleatoriases_ES
dc.titleNew advances in the estimation problem in systems with random failuresen_EN
dc.title.alternativeAportacione al problema de estimación en sistemas con fallos aleatorioses_ES
dc.typedoctoral thesis
dc.subject.udc519.2es_ES
dc.subject.udc12es_ES
europeana.typeTEXT
europeana.dataProviderUniversidad de Granada. España.
europeana.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.rights.accessRightsopen access


Ficheros en el ítem

[PDF]

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Tesis
    Tesis leídas en la Universidad de Granada

Mostrar el registro sencillo del ítem

Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Excepto si se señala otra cosa, la licencia del ítem se describe como Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License