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

Liao, Qixiang; Sheng, Zheng; Shi, Hanqing

doi:10.3390/atmos7010012

Cited by 3 publications

(10 citation statements)

References 21 publications

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“…Hence, we used NSGA-II-a multiobjective optimization algorithm-to search for the best fit [35,43]. Considering that NSGA-II is an improvement of NSGA, one of the first evolutionary algorithms (EAs) for finding multiple Pareto-optimal solutions, the former retains premature convergence, which is a common problem with conventional GAs.…”

Section: Proposed Nssaga Algorithmmentioning

confidence: 99%

“…For different antenna heights, the same values of the refractivity parameters may produce a different set of solutions. Thus, when inferring refractivity parameters based on data sets collected from antenna of differing height, we must sort the individuals according to whether the To reconstruct the refractivity profile, a multiobjective cost function [35] (Liao et al, 2016) is usually defined as:…”

Section: Modeling Refractivity Profile and Objective Functionmentioning

confidence: 99%

“…The excess phase was estimated using a modified version of the Bernese v5.0 software, and the propagation loss was calculated according to the receiving power density of the antenna [35]. Zero-differenced observations were processed in precise point positioning module and the tropospheric zenith delay (phase delay) module produces cleaned post-fit residuals and forms baselines.…”

Section: Experimental Data Testingmentioning

confidence: 99%

“…Therefore, Wu et al [34] tried to combine the GPS phase delay and bending angle to retrieve refractivity profiles, but they neglected the fact that the observed phase delay and bending angles are not independent. For this reason, Liao et al [35] used the non-dominated sorting genetic algorithm II (NSGA-II) to retrieve the atmospheric refractivity structure based on the ground-based GPS phase delay and propagation loss. Although the combination of ground-based GPS phase delay and propagation loss can infer refractivity parameters effectively, its performance is limited by the weak global search capability of NSGA-II.…”

Section: Introductionmentioning

confidence: 99%

“…The present paper is a sequel to Liao et al [35] and is aimed at quantifying the performance of the joint inversion for two types of observation using improved NSGA-II. Here, the objective function is match-field processing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

et al. 2018

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Abstract:The propagation of Global Positioning System (GPS) signals at low-elevation angles is significantly affected by a surface duct. In this paper, we present an improved algorithm known as NSSAGA, in which simulated annealing (SA) is combined with the non-dominated sorting genetic algorithm II (NSGA-II). Matched-field processing was used to remotely sense the refractivity structure by using the data of ground-based GPS phase delay and propagation loss from multiple antenna heights. The performance was checked by simulation data with and without noise. In comparison with NSGA-II, the new hybrid algorithm retrieved the refractivity structure more efficiently under various noise conditions. We then modified the objective function and found that matched-field processing is more effective than the conventional least-squares method for inferring the refractivity parameters. Comparing the inversion results and in situ sounding data suggests that the improved method presented herein can capture refractivity characteristics in realistic environments.

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Section: Proposed Nssaga Algorithmmentioning

confidence: 99%

Section: Modeling Refractivity Profile and Objective Functionmentioning

confidence: 99%

Section: Experimental Data Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

et al. 2018

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Tropospheric Refractivity Profile Estimation by GNSS Measurement at China Big-Triangle Points

Sun¹,

Zhu²,

Lin³

et al. 2021

Atmosphere

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Atmospheric radio refractivity has an obvious influence on the signal transmission path and communication group delay effect. The uncertainty of water vapor distribution is the main reason for the large error of tropospheric refractive index modeling. According to the distribution and characteristics of water vapor pressure, temperature, and pressure, which are the basic components of the refractive index, a method for retrieving atmospheric refractivity profile based on GNSS (Global Navigation Satellite System) and meteorological sensor measurement is introduced and investigated in this study. The variation of the correlation between zenith wet delay and water vapor pressure is investigated and analyzed in detail. The partial pressure profiles of water vapor are retrieved with relevance vector machine method based on tropospheric zenith wet delay calculated by single ground-based GPS (Global Positioning System) receiver. The atmospheric temperature and pressure is calculated with the least square method, which is used to fit the coefficients of the polynomial model based on a large number of historical meteorological radiosonde data of local stations. By combining the water vapor pressure profile retrieving from single ground-based GPS and temperature and pressure profile from reference model, the refractivity profile can be obtained, which is compared to radiosonde measurements. The comparison results show that results of the proposed method are consistent with the results of radiosonde. By using over ten years’ (through 2008 to 2017) historical radiosonde meteorological data of different months at China Big-Triangle Points, i.e., Qingdao, Sanya, Kashi, and Jiamusi radiosonde stations, tropospheric radio refractivity profiles are retrieved and modeled. The comparison results present that the accuracies of refractivity profile of the proposed method at Qingdao, Sanya, Kashi, and Jiamusi are about 5.48, 5.63, 3.58, and 3.78 N-unit, respectively, and the annual average relative RMSE of refractivity at these stations are about 1.66, 1.53, 1.49, and 1.23%, respectively.

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A Comparative Study on Evolutionary Multi-objective Optimization Algorithms Estimating Surface Duct

Liao

Sheng

Shi

et al. 2018

Sensors

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The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. The diversity and convergence of solution sets are evaluated for seven multi-objective evolutionary algorithms with three performance metrics: Hypervolume (HV), Inverted Generational Distance (IGD), and the averaged Hausdorff distance (Δ2). The inversion results are compared with the simulation results, and the experimental comparison is conducted on three groups of test situations. The results demonstrate that the ranking of algorithm performance varies because of the different methods used to calculate performance metrics. Moreover, when the algorithms show overwhelming performance using performance metrics, the inversion result is not more close to the real value. In the comparison of computational experiments, it was found that, as the retrieved parameter dimension increases, the inversion result becomes more unstable. When the observed data are sufficient, the inversion result seems to be improved.

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

Cited by 3 publications

References 21 publications

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

Tropospheric Refractivity Profile Estimation by GNSS Measurement at China Big-Triangle Points

A Comparative Study on Evolutionary Multi-objective Optimization Algorithms Estimating Surface Duct

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