Simulation of Atmospheric Refractive Profile Retrieving from Low‐Elevation Ground‐Based GPS Observations

Wu, Yiyi; Zhang, Hong; Guo, Peng; Zheng, Jie

doi:10.1002/cjg2.1533

Cited by 8 publications

(4 citation statements)

References 8 publications

(7 reference statements)

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“…However, only one type of observational data is used in these methods, such as the GPS signal strength or phase delay. 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.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Simplified models based on a mass of historical detecting data have been studied extensively, for example, linear model, exponential model, double exponential model, Hopfield model, and subsection model [1][2][3]. A retrieval algorithm using ground-based global positioning system (GPS) is developed to establish the atmospheric refractivity profile model [4][5][6]. This method has high accuracy, but high searching volume and high time-consuming.…”

Section: Introductionmentioning

confidence: 99%

A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

Ma¹,

Liu²,

Zhang³

2019

PIER M

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A method based on the radial basis function neural network (RBFNN) is developed to fast establish the tropospheric refractivity profile model. Parameters of the RBFNN include SPREAD, and the number of training samples is optimized. The actual measured data of meteorological station at Qingdao city in China are used as test data to evaluate the performance of RBFNN. The simulation results show that the root mean squared error (RMSE) has a minimum of 0.81 when SPREAD is 8.1. The simulated values agree well with the test data which is observed by using the sounding balloon method. Finally, the tropospheric refractivity profile model of a selected area is established by using two different simulation methods. This paper attempts to propose a method to fast establish the tropospheric refractivity profile model which provides an available method to correct the atmospheric refraction error in radar applications.

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“…These retrieved results were calculated from the single observation. Wu et al [13] tried using the phase delay and bending angle to retrieve the refractivity profile and obtained a relatively accurate result. However, this method did not highlight the superiority of joint inversion due to the strong correlation between the two observations.…”

Section: Introductionmentioning

confidence: 99%

“…The first kind is based on geophysical observation with the same physical characteristics, for example as in the joint inversion with phase delay and bending angle proposed by Wu et al [13]. The two kinds of information have such a strong relationship that they can be regarded as the same data expressed in two different forms.…”

Section: Introductionmentioning

confidence: 99%

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

2016

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Abstract: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 1 , c 2 ) and the vertical altitude (h 1 , h 2 ). 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.

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Simulation of Atmospheric Refractive Profile Retrieving from Low‐Elevation Ground‐Based GPS Observations

Cited by 8 publications

References 8 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

A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

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

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