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: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718815848 |
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| _version_ | 1832547810200780800 |
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| author | Huazheng Du Guoye Chen Xuegang Hu Na Xia Biaodian Xu |
| author_facet | Huazheng Du Guoye Chen Xuegang Hu Na Xia Biaodian Xu |
| author_sort | Huazheng Du |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-ce609dc005464bbcadf9de155588851a |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ce609dc005464bbcadf9de155588851a2025-02-03T06:43:16ZengWileyInternational 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 |
| 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 |
| title | 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_short | 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 |
| 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 |