Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks

In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal,...
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Gespeichert in:

Autor*in:	Xiaoyu Liu [verfasserIn] Tong Wang [verfasserIn] Jinming Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 16, p 3920
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:16, p 3920

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14163920

Katalog-ID:	DOAJ079193382

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allfields	10.3390/rs14163920 doi (DE-627)DOAJ079193382 (DE-599)DOAJ3bc2d652db174c739f14d7a492a55206 DE-627 ger DE-627 rakwb eng Xiaoyu Liu verfasserin aut Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development. distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix Science Q Tong Wang verfasserin aut Jinming Chen verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 3920 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 3920 https://doi.org/10.3390/rs14163920 kostenfrei https://doaj.org/article/3bc2d652db174c739f14d7a492a55206 kostenfrei https://www.mdpi.com/2072-4292/14/16/3920 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 3920
spelling	10.3390/rs14163920 doi (DE-627)DOAJ079193382 (DE-599)DOAJ3bc2d652db174c739f14d7a492a55206 DE-627 ger DE-627 rakwb eng Xiaoyu Liu verfasserin aut Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development. distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix Science Q Tong Wang verfasserin aut Jinming Chen verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 3920 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 3920 https://doi.org/10.3390/rs14163920 kostenfrei https://doaj.org/article/3bc2d652db174c739f14d7a492a55206 kostenfrei https://www.mdpi.com/2072-4292/14/16/3920 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 3920
allfields_unstemmed	10.3390/rs14163920 doi (DE-627)DOAJ079193382 (DE-599)DOAJ3bc2d652db174c739f14d7a492a55206 DE-627 ger DE-627 rakwb eng Xiaoyu Liu verfasserin aut Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development. distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix Science Q Tong Wang verfasserin aut Jinming Chen verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 3920 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 3920 https://doi.org/10.3390/rs14163920 kostenfrei https://doaj.org/article/3bc2d652db174c739f14d7a492a55206 kostenfrei https://www.mdpi.com/2072-4292/14/16/3920 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 3920
allfieldsGer	10.3390/rs14163920 doi (DE-627)DOAJ079193382 (DE-599)DOAJ3bc2d652db174c739f14d7a492a55206 DE-627 ger DE-627 rakwb eng Xiaoyu Liu verfasserin aut Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development. distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix Science Q Tong Wang verfasserin aut Jinming Chen verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 3920 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 3920 https://doi.org/10.3390/rs14163920 kostenfrei https://doaj.org/article/3bc2d652db174c739f14d7a492a55206 kostenfrei https://www.mdpi.com/2072-4292/14/16/3920 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 3920
allfieldsSound	10.3390/rs14163920 doi (DE-627)DOAJ079193382 (DE-599)DOAJ3bc2d652db174c739f14d7a492a55206 DE-627 ger DE-627 rakwb eng Xiaoyu Liu verfasserin aut Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development. distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix Science Q Tong Wang verfasserin aut Jinming Chen verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 3920 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 3920 https://doi.org/10.3390/rs14163920 kostenfrei https://doaj.org/article/3bc2d652db174c739f14d7a492a55206 kostenfrei https://www.mdpi.com/2072-4292/14/16/3920 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 3920
language	English
source	In Remote Sensing 14(2022), 16, p 3920 volume:14 year:2022 number:16, p 3920
sourceStr	In Remote Sensing 14(2022), 16, p 3920 volume:14 year:2022 number:16, p 3920
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topic_facet	distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Xiaoyu Liu @@aut@@ Tong Wang @@aut@@ Jinming Chen @@aut@@
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author	Xiaoyu Liu
spellingShingle	Xiaoyu Liu misc distributed sensor network misc Bayesian Cramer–Rao lower bound (BCRLB) misc configuration calibration misc parameter identifiability misc Jacobian matrix misc Science misc Q Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks
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topic_title	Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks distributed sensor network Bayesian Cramer–Rao lower bound (BCRLB) configuration calibration parameter identifiability Jacobian matrix
topic	misc distributed sensor network misc Bayesian Cramer–Rao lower bound (BCRLB) misc configuration calibration misc parameter identifiability misc Jacobian matrix misc Science misc Q
topic_unstemmed	misc distributed sensor network misc Bayesian Cramer–Rao lower bound (BCRLB) misc configuration calibration misc parameter identifiability misc Jacobian matrix misc Science misc Q
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title	Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks
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title_sort	identifiability analysis for configuration calibration in distributed sensor networks
title_auth	Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks
abstract	In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development.
abstractGer	In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development.
abstract_unstemmed	In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development.
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title_short	Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks
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