GNSS Height Anomaly Fitting Based on Earth Gravity Field Models and BP Neural Network
The high-precision determination of GNSS height anomalies has important application value in precise engineering surveying.In order to systematically compare the accuracy of different Earth gravity field models and fitting methods in GNSS height anomaly fitting,this paper chooses to compare the accuracy of four high-degree/order Earth gravity field models,EGM2008,XGM2019e,EIGEN-6C4,and SGG-UGM-2,in calculating the height anomaly of the regional model through a certain engineering example.At the same time,the accuracy of traditional cubic surface fitting and BP neural network fitting is compared.Experiments have shown that in this calculation area,the higher the order of the gravity field model,but not the better the accuracy.The EIGEN-6C4 model has the highest accuracy in calculating height anomalies,with an RMS of 35.2 cm.The fitting results of BP neural network have also significantly improved compared to cubic surface fitting,and the optimal external fitting accuracy can reach 1.28 cm.Therefore,combining high-precision Earth gravity field models with BP neural network methods is expected to obtain high-precision height anomalies for the study of other engineering cases.
Earth gravity field modelBP neural networkGNSS height anomalyfitting
NETL
NSTL
万方数据
