Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters

Global positioning system–based meteorological parameters sensing has become a hot topic in the field of satellite navigation application. The major research content is global positioning system radio occultation observation, which utilizes the delay and bending of global positioning system signal t...

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Main Authors:	Huazheng Du, Guoye Chen, Xuegang Hu, Na Xia, Biaodian Xu
Format:	Article
Language:	English
Published:	SAGE Publishing 2018-12-01
Series:	International Journal of Distributed Sensor Networks
Online Access:	https://doi.org/10.1177/1550147718815848

id	doaj-ce609dc005464bbcadf9de155588851a
record_format	Article
spelling	doaj-ce609dc005464bbcadf9de155588851a2020-11-25T03:39:18ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772018-12-011410.1177/1550147718815848Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parametersHuazheng Du0Guoye Chen1Xuegang Hu2Na Xia3Biaodian Xu4School of Computer and Information, Hefei University of Technology, Hefei, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei, ChinaProvince Key Laboratory of Industry Safety and Emergency Technology, Hefei, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei, ChinaGlobal positioning system–based meteorological parameters sensing has become a hot topic in the field of satellite navigation application. The major research content is global positioning system radio occultation observation, which utilizes the delay and bending of global positioning system signal to compute the meteorological parameters (temperature, pressure, and water vapor), so as to improve the accuracy of numerical weather prediction. In this article, the atmospheric parameters computing algorithm based on simultaneous perturbation stochastic approximation is proposed. Perturbation effect is used to obtain the approximate gradient of cost function, which can guide the searching to achieve the optimal solution gradually. The proposed algorithm avoids the complicated derivative computing for the cost function, and without designing the tangent linear and adjoint operators. The algorithm can converge to the optimal or approximately optimal solution quickly. The validity and superiority of this method has been proved by extensive comparative experiment results.https://doi.org/10.1177/1550147718815848
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Huazheng Du Guoye Chen Xuegang Hu Na Xia Biaodian Xu
spellingShingle	Huazheng Du Guoye Chen Xuegang Hu Na Xia Biaodian Xu Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters International Journal of Distributed Sensor Networks
author_facet	Huazheng Du Guoye Chen Xuegang Hu Na Xia Biaodian Xu
author_sort	Huazheng Du
title	Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters
title_short	Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters
title_full	Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters
title_fullStr	Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters
title_full_unstemmed	Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters
title_sort	simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters
publisher	SAGE Publishing
series	International Journal of Distributed Sensor Networks
issn	1550-1477
publishDate	2018-12-01
description	Global positioning system–based meteorological parameters sensing has become a hot topic in the field of satellite navigation application. The major research content is global positioning system radio occultation observation, which utilizes the delay and bending of global positioning system signal to compute the meteorological parameters (temperature, pressure, and water vapor), so as to improve the accuracy of numerical weather prediction. In this article, the atmospheric parameters computing algorithm based on simultaneous perturbation stochastic approximation is proposed. Perturbation effect is used to obtain the approximate gradient of cost function, which can guide the searching to achieve the optimal solution gradually. The proposed algorithm avoids the complicated derivative computing for the cost function, and without designing the tangent linear and adjoint operators. The algorithm can converge to the optimal or approximately optimal solution quickly. The validity and superiority of this method has been proved by extensive comparative experiment results.
url	https://doi.org/10.1177/1550147718815848
work_keys_str_mv	AT huazhengdu simultaneousperturbationstochasticapproximationbasedradiooccultationdataassimilationforsensingatmosphericparameters AT guoyechen simultaneousperturbationstochasticapproximationbasedradiooccultationdataassimilationforsensingatmosphericparameters AT xueganghu simultaneousperturbationstochasticapproximationbasedradiooccultationdataassimilationforsensingatmosphericparameters AT naxia simultaneousperturbationstochasticapproximationbasedradiooccultationdataassimilationforsensingatmosphericparameters AT biaodianxu simultaneousperturbationstochasticapproximationbasedradiooccultationdataassimilationforsensingatmosphericparameters
_version_	1724539643312996352

Simultaneous perturbation stochastic approximation–based radio occultation data assimilation for sensing atmospheric parameters

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