Abstract
Real-time modeling of regional troposphere has attracted considerable research attention in the current GNSS field, and its modeling products play an important role in global navigation satellite system (GNSS) real-time precise positioning and real-time inversion of atmospheric water vapor. Multicore support vector machine (MS) based on genetic optimization algorithm, single-core support vector machine (SVM), four-parameter method (FP), neural network method (BP), and root mean square fusion method (SUM) are used for real-time and final zenith tropospheric delay (ZTD) modeling of Hong Kong CORS network in this study. Real-time ZTD modeling experiment results for five consecutive days showed that the average deviation (bias) and root mean square (RMS) of FP, BP, SVM, and SUM reduced by 48.25%, 54.46%, 41.82%, and 51.82% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. The final ZTD modeling experiment results showed that the bias and RMS of FP, BP, SVM, and SUM reduced by 3.80%, 49.78%, 25.71%, and 49.35% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. Accuracy of the five methods generally reaches millimeter level in most of the time periods. MS demonstrates higher precision and stability in the modeling of stations with an elevation at the average level of the survey area and higher elevation than that of other models. MS, SVM, and SUM exhibit higher precision and stability in the modeling of the station with an elevation at the average level of the survey area than FP. Meanwhile, real-time modeling error distribution of the five methods is significantly better than the final modeling. Standard deviation and average real-time modeling improved by 43.19% and 24.04%, respectively.
Highlights
Zenith tropospheric delay (ZTD) correction methods can generally be divided into model correction [1], parameter estimation [2], and external correction methods [3]. e parameter estimation method is generally used in precise point positioning (PPP) and long baseline solution, and other methods are needed to obtain ZTD prior value before parameter selection. e external correction method requires expensive equipment and demonstrates difficulty in obtaining accurate results in a short time. e model correction method is the focus of this study
It is successfully employed in ZTD modeling to overcome the shortcomings of the above two types of ZTD models; A GPS tropospheric delay interpolation model based on radial basis function (RBF) neural network was constructed, and the accuracy of tropospheric delay interpolation of the model can reach the millimeter level with continuously operating reference stations (CORS) station data of Anhui power system [9]
(b) e daily modeling accuracy of the BP model exhibits a slightly greater change than the four other modeling methods. e ZTD modeling experiment results of six observation stations for five consecutive days showed that the average bias and root mean square (RMS) values of four-parameter method (FP), BP, support vector machine (SVM), and square fusion method (SUM) reduced by 48.25%, 54.46%, 41.82%, and 51.82% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with those of MS
Summary
Zenith tropospheric delay (ZTD) correction methods can generally be divided into model correction [1], parameter estimation [2], and external correction methods [3]. e parameter estimation method is generally used in precise point positioning (PPP) and long baseline solution, and other methods are needed to obtain ZTD prior value before parameter selection. e external correction method requires expensive equipment and demonstrates difficulty in obtaining accurate results in a short time. e model correction method is the focus of this study. Regional ZTD modeling based on ML method has the advantages of better describing the nonlinear variation law of ZTD, conducting high-precision and high-stability modeling in a large area without measuring meteorological parameters and better utilizing meteorological big data for data mining. It is successfully employed in ZTD modeling to overcome the shortcomings of the above two types of ZTD models; A GPS tropospheric delay interpolation model based on radial basis function (RBF) neural network was constructed, and the accuracy of tropospheric delay interpolation of the model can reach the millimeter level with CORS station data of Anhui power system [9]. E real-time ZTD of each CORS station is calculated with real-time PPP technology based on the variance component in [22] to realize the practical application of the model without measured meteorological parameters, and Hong Kong regional CORS (15 km scale) is used as an example. e real-time modeling of ZTD in the Hong Kong region is carried out with multicore support vector machine (MS) based on genetic optimization algorithm, single-core support vector machine (SVM), four-parameter method (FP), neural network method (BP), and root mean square fusion method (SUM). e model accuracy is evaluated, and the real-time and high-precision regional ZTD model is further studied using artificial intelligence technology
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
