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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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 |
_version_ |
1716793055160303616 |