Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by on...
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Autor*in:	Raquel Caballero-Águila [verfasserIn] Aurora Hermoso-Carazo [verfasserIn] Josefa Linares-Pérez [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	fusion estimation sensor networks random parameter matrices multiplicative noises random delays

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 5(2017), 3, p 45
Übergeordnetes Werk:	volume:5 ; year:2017 ; number:3, p 45

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math5030045

Katalog-ID:	DOAJ002430495

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allfields	10.3390/math5030045 doi (DE-627)DOAJ002430495 (DE-599)DOAJe32e1f9ee94241219bee5d1f4155b9dc DE-627 ger DE-627 rakwb eng QA1-939 Raquel Caballero-Águila verfasserin aut Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example. fusion estimation sensor networks random parameter matrices multiplicative noises random delays Mathematics Aurora Hermoso-Carazo verfasserin aut Josefa Linares-Pérez verfasserin aut In Mathematics MDPI AG, 2013 5(2017), 3, p 45 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:5 year:2017 number:3, p 45 https://doi.org/10.3390/math5030045 kostenfrei https://doaj.org/article/e32e1f9ee94241219bee5d1f4155b9dc kostenfrei https://www.mdpi.com/2227-7390/5/3/45 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2017 3, p 45
spelling	10.3390/math5030045 doi (DE-627)DOAJ002430495 (DE-599)DOAJe32e1f9ee94241219bee5d1f4155b9dc DE-627 ger DE-627 rakwb eng QA1-939 Raquel Caballero-Águila verfasserin aut Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example. fusion estimation sensor networks random parameter matrices multiplicative noises random delays Mathematics Aurora Hermoso-Carazo verfasserin aut Josefa Linares-Pérez verfasserin aut In Mathematics MDPI AG, 2013 5(2017), 3, p 45 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:5 year:2017 number:3, p 45 https://doi.org/10.3390/math5030045 kostenfrei https://doaj.org/article/e32e1f9ee94241219bee5d1f4155b9dc kostenfrei https://www.mdpi.com/2227-7390/5/3/45 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2017 3, p 45
allfields_unstemmed	10.3390/math5030045 doi (DE-627)DOAJ002430495 (DE-599)DOAJe32e1f9ee94241219bee5d1f4155b9dc DE-627 ger DE-627 rakwb eng QA1-939 Raquel Caballero-Águila verfasserin aut Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example. fusion estimation sensor networks random parameter matrices multiplicative noises random delays Mathematics Aurora Hermoso-Carazo verfasserin aut Josefa Linares-Pérez verfasserin aut In Mathematics MDPI AG, 2013 5(2017), 3, p 45 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:5 year:2017 number:3, p 45 https://doi.org/10.3390/math5030045 kostenfrei https://doaj.org/article/e32e1f9ee94241219bee5d1f4155b9dc kostenfrei https://www.mdpi.com/2227-7390/5/3/45 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2017 3, p 45
allfieldsGer	10.3390/math5030045 doi (DE-627)DOAJ002430495 (DE-599)DOAJe32e1f9ee94241219bee5d1f4155b9dc DE-627 ger DE-627 rakwb eng QA1-939 Raquel Caballero-Águila verfasserin aut Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example. fusion estimation sensor networks random parameter matrices multiplicative noises random delays Mathematics Aurora Hermoso-Carazo verfasserin aut Josefa Linares-Pérez verfasserin aut In Mathematics MDPI AG, 2013 5(2017), 3, p 45 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:5 year:2017 number:3, p 45 https://doi.org/10.3390/math5030045 kostenfrei https://doaj.org/article/e32e1f9ee94241219bee5d1f4155b9dc kostenfrei https://www.mdpi.com/2227-7390/5/3/45 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2017 3, p 45
allfieldsSound	10.3390/math5030045 doi (DE-627)DOAJ002430495 (DE-599)DOAJe32e1f9ee94241219bee5d1f4155b9dc DE-627 ger DE-627 rakwb eng QA1-939 Raquel Caballero-Águila verfasserin aut Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example. fusion estimation sensor networks random parameter matrices multiplicative noises random delays Mathematics Aurora Hermoso-Carazo verfasserin aut Josefa Linares-Pérez verfasserin aut In Mathematics MDPI AG, 2013 5(2017), 3, p 45 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:5 year:2017 number:3, p 45 https://doi.org/10.3390/math5030045 kostenfrei https://doaj.org/article/e32e1f9ee94241219bee5d1f4155b9dc kostenfrei https://www.mdpi.com/2227-7390/5/3/45 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2017 3, p 45
language	English
source	In Mathematics 5(2017), 3, p 45 volume:5 year:2017 number:3, p 45
sourceStr	In Mathematics 5(2017), 3, p 45 volume:5 year:2017 number:3, p 45
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	fusion estimation sensor networks random parameter matrices multiplicative noises random delays Mathematics
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container_title	Mathematics
authorswithroles_txt_mv	Raquel Caballero-Águila @@aut@@ Aurora Hermoso-Carazo @@aut@@ Josefa Linares-Pérez @@aut@@
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callnumber-first	Q - Science
author	Raquel Caballero-Águila
spellingShingle	Raquel Caballero-Águila misc QA1-939 misc fusion estimation misc sensor networks misc random parameter matrices misc multiplicative noises misc random delays misc Mathematics Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission
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topic_title	QA1-939 Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission fusion estimation sensor networks random parameter matrices multiplicative noises random delays
topic	misc QA1-939 misc fusion estimation misc sensor networks misc random parameter matrices misc multiplicative noises misc random delays misc Mathematics
topic_unstemmed	misc QA1-939 misc fusion estimation misc sensor networks misc random parameter matrices misc multiplicative noises misc random delays misc Mathematics
topic_browse	misc QA1-939 misc fusion estimation misc sensor networks misc random parameter matrices misc multiplicative noises misc random delays misc Mathematics
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title	Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission
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title_full	Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission
author_sort	Raquel Caballero-Águila
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title_auth	Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission
abstract	In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.
abstractGer	In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.
abstract_unstemmed	In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.
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title_short	Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission
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