Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation Loss

In this paper, we present a new estimation of the atmospheric refractivity profile combining the scattering signal (electromagnetic wave propagation loss) and the direct signal (phase delay). The refractivity profile is modeled using four parameters, i.e., the gradient of the refractivity profile (c...

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Main Authors: Qixiang Liao, Zheng Sheng, Hanqing Shi
Format: Article
Language:English
Published: MDPI AG 2016-01-01
Series:Atmosphere
Subjects:
Online Access:http://www.mdpi.com/2073-4433/7/1/12
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spelling doaj-87bd586d3e8c473183d2e2db7be077a22020-11-24T20:54:59ZengMDPI AGAtmosphere2073-44332016-01-01711210.3390/atmos7010012atmos7010012Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation LossQixiang Liao0Zheng Sheng1Hanqing Shi2College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, ChinaCollege of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, ChinaCollege of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, ChinaIn this paper, we present a new estimation of the atmospheric refractivity profile combining the scattering signal (electromagnetic wave propagation loss) and the direct signal (phase delay). The refractivity profile is modeled using four parameters, i.e., the gradient of the refractivity profile (c1, c2) and the vertical altitude (h1, h2). We apply the NSGA-II (Non-dominated Sorting Genetic Algorithm II), a multiobjective optimization algorithm, to achieve the goals of joint optimization inversion in the inverting process, and compare this method with traditional individual inversion methods. The anti-noise ability of joint inversion is investigated under the noiseless condition and adding noise condition, respectively. The numerical experiments demonstrate that joint inversion is superior to individual inversion. The adding noise test further suggests that this method can estimate synthesized parameters more efficiently and accurately in different conditions. Finally, a set of measured data is tested in the new way and the consequence of inversion shows the joint optimization inversion algorithm has feasibility, effectiveness and superiority in the retrieval of the refractivity profile.http://www.mdpi.com/2073-4433/7/1/12atmospheric ductglobal positioning system (GPS)joint inversionrefractivity profileremote sensing
collection DOAJ
language English
format Article
sources DOAJ
author Qixiang Liao
Zheng Sheng
Hanqing Shi
spellingShingle Qixiang Liao
Zheng Sheng
Hanqing Shi
Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation Loss
Atmosphere
atmospheric duct
global positioning system (GPS)
joint inversion
refractivity profile
remote sensing
author_facet Qixiang Liao
Zheng Sheng
Hanqing Shi
author_sort Qixiang Liao
title Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation Loss
title_short Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation Loss
title_full Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation Loss
title_fullStr Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation Loss
title_full_unstemmed Joint Inversion of Atmospheric Refractivity Profile Based on Ground-Based GPS Phase Delay and Propagation Loss
title_sort joint inversion of atmospheric refractivity profile based on ground-based gps phase delay and propagation loss
publisher MDPI AG
series Atmosphere
issn 2073-4433
publishDate 2016-01-01
description In this paper, we present a new estimation of the atmospheric refractivity profile combining the scattering signal (electromagnetic wave propagation loss) and the direct signal (phase delay). The refractivity profile is modeled using four parameters, i.e., the gradient of the refractivity profile (c1, c2) and the vertical altitude (h1, h2). We apply the NSGA-II (Non-dominated Sorting Genetic Algorithm II), a multiobjective optimization algorithm, to achieve the goals of joint optimization inversion in the inverting process, and compare this method with traditional individual inversion methods. The anti-noise ability of joint inversion is investigated under the noiseless condition and adding noise condition, respectively. The numerical experiments demonstrate that joint inversion is superior to individual inversion. The adding noise test further suggests that this method can estimate synthesized parameters more efficiently and accurately in different conditions. Finally, a set of measured data is tested in the new way and the consequence of inversion shows the joint optimization inversion algorithm has feasibility, effectiveness and superiority in the retrieval of the refractivity profile.
topic atmospheric duct
global positioning system (GPS)
joint inversion
refractivity profile
remote sensing
url http://www.mdpi.com/2073-4433/7/1/12
work_keys_str_mv AT qixiangliao jointinversionofatmosphericrefractivityprofilebasedongroundbasedgpsphasedelayandpropagationloss
AT zhengsheng jointinversionofatmosphericrefractivityprofilebasedongroundbasedgpsphasedelayandpropagationloss
AT hanqingshi jointinversionofatmosphericrefractivityprofilebasedongroundbasedgpsphasedelayandpropagationloss
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